Merge pull request #378 from MervinPraison/develop

Enhance train.py with Configurable Training Parameters
2026-11-08 08:11:07 -10:00 · 2025-02-06 07:48:00 +00:00 · 2025-02-06 07:47:40 +00:00 · 2025-02-06 07:26:59 +00:00 · 2025-02-06 07:26:41 +00:00 · 2025-02-06 07:19:11 +00:00
10 changed files with 303 additions and 77 deletions
@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.64 gunicorn markdown
+RUN pip install flask praisonai==2.0.73 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]
@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
            file.write(&#34;FROM python:3.11-slim\n&#34;)
            file.write(&#34;WORKDIR /app\n&#34;)
            file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.64 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.73 gunicorn markdown\n&#34;)
            file.write(&#34;EXPOSE 8080\n&#34;)
            file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
            
@@ -20,6 +20,20 @@ icon: "dumbbell"
 export HF_TOKEN=xxxxxxxxxxxxxx
 ```

+## Initilise praisonai train
+
+```bash
+praisonai train init
+```
+
+## Required
+
+1. Huggingface token
+2. Base model to train on (e.g. unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit)
+3. Dataset to train on (e.g. yahma/alpaca-cleaned)
+4. Huggingface model name to upload to (e.g. mervinpraison/llama3.1-instruct) (Optional)
+5. Ollama model name to upload to (e.g. mervinpraison/llama3.1-instruct) (Optional)
+
 ## To upload to ollama.com (Linux)

 ```bash
@@ -108,4 +122,21 @@ quantization_method:

 ```bash
 praisonai train
+```
+
+## wandb
+
+```bash
+wandb login
+```
+
+<Note>
+Get the key from [here](https://wandb.ai/site/login)
+</Note>
+
+```bash
+export PRAISON_WANDB=True
+export WANDB_LOG_MODEL=checkpoint
+export WANDB_PROJECT=praisonai-test
+export PRAISON_WANDB_RUN_NAME=praisonai-train  
 ```
@@ -3,7 +3,7 @@ class Praisonai < Formula
  
    desc "AI tools for various AI applications"
    homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.64.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.73.tar.gz"
    sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
    license "MIT"
  
@@ -25,7 +25,6 @@ CALL_MODULE_AVAILABLE = False
 CREWAI_AVAILABLE = False
 AUTOGEN_AVAILABLE = False
 PRAISONAI_AVAILABLE = False
-TRAIN_AVAILABLE = False
 try:
    # Create necessary directories and set CHAINLIT_APP_ROOT
    if "CHAINLIT_APP_ROOT" not in os.environ:
@@ -72,12 +71,6 @@ try:
 except ImportError:
    pass

-try:
-    import accelerate
-    TRAIN_AVAILABLE = True
-except ImportError:
-    pass
-
 logging.basicConfig(level=os.environ.get('LOGLEVEL', 'INFO'), format='%(asctime)s - %(levelname)s - %(message)s')
 logging.getLogger('alembic').setLevel(logging.ERROR)
 logging.getLogger('gradio').setLevel(logging.ERROR)
@@ -196,8 +189,7 @@ class PraisonAI:
            package_root = os.path.dirname(os.path.abspath(__file__))
            config_yaml_destination = os.path.join(os.getcwd(), 'config.yaml')

-            # Create config.yaml only if it doesn't exist or --model or --dataset is provided
-            if not os.path.exists(config_yaml_destination) or args.model or args.dataset:
+            if not os.path.exists(config_yaml_destination):
                config = generate_config(
                    model_name=args.model,
                    hf_model_name=args.hf,
@@ -208,6 +200,20 @@ class PraisonAI:
                )
                with open('config.yaml', 'w') as f:
                    yaml.dump(config, f, default_flow_style=False, indent=2)
+            elif args.model or args.hf or args.ollama or (args.dataset and args.dataset != "yahma/alpaca-cleaned"):
+                config = generate_config(
+                    model_name=args.model,
+                    hf_model_name=args.hf,
+                    ollama_model_name=args.ollama,
+                    dataset=[{
+                        "name": args.dataset
+                    }]
+                )
+                with open('config.yaml', 'w') as f:
+                    yaml.dump(config, f, default_flow_style=False, indent=2)
+            else:
+                with open(config_yaml_destination, 'r') as f:
+                    config = yaml.safe_load(f)

            # Overwrite huggingface_save and ollama_save if --hf or --ollama are provided
            if args.hf:
@@ -399,13 +405,8 @@ class PraisonAI:
                sys.exit(0)

            elif args.command == 'train':
-                if not TRAIN_AVAILABLE:
-                    print("[red]ERROR: Train feature is not installed. Install with:[/red]")
-                    print("\npip install \"praisonai[train]\"\n")
-                    sys.exit(1)
                package_root = os.path.dirname(os.path.abspath(__file__))
                config_yaml_destination = os.path.join(os.getcwd(), 'config.yaml')
-                

            elif args.command == 'ui':
                if not CHAINLIT_AVAILABLE:
@@ -56,7 +56,7 @@ class CloudDeployer:
            file.write("FROM python:3.11-slim\n")
            file.write("WORKDIR /app\n")
            file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.0.64 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.73 gunicorn markdown\n")
            file.write("EXPOSE 8080\n")
            file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
            
@@ -12,7 +12,7 @@ elif [[ "$OSTYPE" == "linux-gnu"* ]]; then
    # Linux
    MINICONDA_URL="https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh"
    
-    # Check and install libcurl development package if not present
+    # Install libcurl development package if not present (Debian based)
    if command -v dpkg &> /dev/null; then
        if ! dpkg -s libcurl4-openssl-dev &> /dev/null; then
            echo "libcurl4-openssl-dev is not installed. Installing..."
@@ -25,6 +25,24 @@ elif [[ "$OSTYPE" == "linux-gnu"* ]]; then
        echo "Non-Debian based Linux detected. Please ensure libcurl development libraries are installed."
    fi

+    # Check if ollama is installed and executable; if not, install it
+    if ! command -v ollama &> /dev/null; then
+        echo "Ollama is not installed. Installing Ollama..."
+        curl -fsSL https://ollama.com/install.sh | sh
+        
+        # Generate SSH key non-interactively only if it doesn't already exist
+        if [ ! -f ~/.ssh/id_ed25519 ]; then
+            echo "Generating SSH key for Ollama..."
+            ssh-keygen -t ed25519 -N "" -f ~/.ssh/id_ed25519 -q
+        else
+            echo "SSH key ~/.ssh/id_ed25519 already exists. Skipping generation."
+        fi
+        echo "Copying SSH key to /usr/share/ollama/.ollama..."
+        sudo cp ~/.ssh/id_ed25519 /usr/share/ollama/.ollama
+    else
+        echo "Ollama is already installed."
+    fi
+
 elif [[ "$OSTYPE" == "msys" || "$OSTYPE" == "win32" ]]; then
    # Windows
    MINICONDA_URL="https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe"
@@ -35,12 +53,12 @@ else
    exit 1
 fi

-# Check if conda is already installed
+# Check if conda is installed
 if ! command -v conda &> /dev/null; then
    echo "Conda is not installed. Installing Miniconda..."
-    wget $MINICONDA_URL -O ~/miniconda.sh
-    bash ~/miniconda.sh -b -p $HOME/miniconda
-    source $HOME/miniconda/bin/activate
+    wget "$MINICONDA_URL" -O ~/miniconda.sh
+    bash ~/miniconda.sh -b -p "$HOME/miniconda"
+    source "$HOME/miniconda/bin/activate"
    conda init
 else
    echo "Conda is already installed."
@@ -48,44 +66,40 @@ fi

 # Create and activate the Conda environment
 ENV_NAME="praison_env"
-if conda info --envs | grep -q $ENV_NAME; then
+if conda info --envs | grep -q "$ENV_NAME"; then
    echo "Environment $ENV_NAME already exists. Recreating..."
-    conda env remove -y -n $ENV_NAME  # Remove existing environment
+    conda env remove -y -n "$ENV_NAME"
    if [[ "$OSTYPE" == "darwin"* ]]; then
-        # macOS (both Intel and M1/M2)
-        conda create --name $ENV_NAME python=3.10 pytorch=2.3.0 -c pytorch -y
+        conda create --name "$ENV_NAME" python=3.10 pytorch=2.3.0 -c pytorch -y
    elif [[ "$OSTYPE" == "linux-gnu"* ]]; then
-        # Linux
-        conda create --name $ENV_NAME python=3.10 pytorch=2.3.0 cudatoolkit=11.8 -c pytorch -c nvidia -y
+        conda create --name "$ENV_NAME" python=3.10 pytorch=2.3.0 cudatoolkit=11.8 -c pytorch -c nvidia -y
    fi
 else
    echo "Creating new environment $ENV_NAME..."
    if [[ "$OSTYPE" == "darwin"* ]]; then
-        # macOS (both Intel and M1/M2)
-        conda create --name $ENV_NAME python=3.10 pytorch=2.3.0 -c pytorch -y
+        conda create --name "$ENV_NAME" python=3.10 pytorch=2.3.0 -c pytorch -y
    elif [[ "$OSTYPE" == "linux-gnu"* ]]; then
-        # Linux
-        conda create --name $ENV_NAME python=3.10 pytorch=2.3.0 cudatoolkit=11.8 -c pytorch -c nvidia -y
+        conda create --name "$ENV_NAME" python=3.10 pytorch=2.3.0 cudatoolkit=11.8 -c pytorch -c nvidia -y
    fi
 fi

 # Activate the environment
-source $HOME/miniconda/bin/activate $ENV_NAME
+source "$HOME/miniconda/bin/activate" "$ENV_NAME"

 # Install cmake via conda
 echo "Installing cmake..."
 conda install -y cmake

 # Get full path of pip within the activated environment
-PIP_FULL_PATH=$(conda run -n $ENV_NAME which pip)
+PIP_FULL_PATH=$(conda run -n "$ENV_NAME" which pip)

-# Install other packages within the activated environment using pip
+# Install other packages using pip
 $PIP_FULL_PATH install --upgrade pip 
 $PIP_FULL_PATH install "xformers==0.0.26.post1"
 $PIP_FULL_PATH install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git@038e6d4c8d40207a87297ab3aaf787c19b1006d1"
 $PIP_FULL_PATH install --no-deps "trl<0.9.0" peft accelerate bitsandbytes 
 $PIP_FULL_PATH install unsloth_zoo
 $PIP_FULL_PATH install cut_cross_entropy
-$PIP_FULL_PATH install sentencepiece protobuf datasets huggingface_hub hf_transfer
+$PIP_FULL_PATH install sentencepiece protobuf datasets huggingface_hub hf_transfer wandb

 echo "Setup completed successfully!"
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
-This script finetunes a model using Unsloth’s fast training framework.
+This script finetunes a model using Unsloth's fast training framework.
 It supports both ShareGPT and Alpaca‑style datasets by converting raw conversation
 data into plain-text prompts using a chat template, then pre‑tokenizing the prompts.
 Extra debug logging is added to help trace the root cause of errors.
@@ -27,7 +27,7 @@ from functools import partial
 #####################################
 def formatting_prompts_func(examples, tokenizer):
    """
-    Converts each example’s conversation into a single plain-text prompt.
+    Converts each example's conversation into a single plain-text prompt.
    If the example has a "conversations" field, process it as ShareGPT-style.
    Otherwise, assume Alpaca-style data with "instruction", "input", and "output" fields.
    """
@@ -217,21 +217,23 @@ class TrainModel:
        tokenized_dataset = self.tokenize_dataset(raw_dataset)
        print("DEBUG: Dataset tokenization complete.")
        training_args = TrainingArguments(
-            per_device_train_batch_size=2,
-            gradient_accumulation_steps=4,
-            warmup_steps=5,
-            max_steps=60,
-            learning_rate=2e-4,
-            fp16=not is_bfloat16_supported(),
-            bf16=is_bfloat16_supported(),
-            logging_steps=1,
-            optim="adamw_8bit",
-            weight_decay=0.01,
-            lr_scheduler_type="linear",
-            seed=3407,
-            output_dir="outputs",
-            report_to="none",
-            remove_unused_columns=False,
+            per_device_train_batch_size=self.config.get("per_device_train_batch_size", 2),
+            gradient_accumulation_steps=self.config.get("gradient_accumulation_steps", 2),
+            warmup_steps=self.config.get("warmup_steps", 50),
+            max_steps=self.config.get("max_steps", 2800),
+            learning_rate=self.config.get("learning_rate", 2e-4),
+            fp16=self.config.get("fp16", not is_bfloat16_supported()),
+            bf16=self.config.get("bf16", is_bfloat16_supported()),
+            logging_steps=self.config.get("logging_steps", 15),
+            optim=self.config.get("optim", "adamw_8bit"),
+            weight_decay=self.config.get("weight_decay", 0.01),
+            lr_scheduler_type=self.config.get("lr_scheduler_type", "linear"),
+            seed=self.config.get("seed", 3407),
+            output_dir=self.config.get("output_dir", "outputs"),
+            report_to="none" if not os.getenv("PRAISON_WANDB") else "wandb",
+            save_steps=self.config.get("save_steps", 100) if os.getenv("PRAISON_WANDB") else None,
+            run_name=os.getenv("PRAISON_WANDB_RUN_NAME", "praisonai-train") if os.getenv("PRAISON_WANDB") else None,
+            remove_unused_columns=self.config.get("remove_unused_columns", False),
        )
        # Since the dataset is pre-tokenized, we supply a dummy dataset_text_field.
        trainer = SFTTrainer(
@@ -312,26 +314,204 @@ class TrainModel:

    def prepare_modelfile_content(self):
        output_model = self.config["hf_model_name"]
-        gguf_path = f"{output_model}/unsloth.Q4_K_M.gguf"
-        if not os.path.exists(gguf_path):
-            self.model, self.hf_tokenizer = self.load_model()
-            self.save_model_gguf()
-        return f"""FROM {output_model}/unsloth.Q4_K_M.gguf
-
-TEMPLATE \"\"\"Below are some instructions that describe some tasks. Write responses that appropriately complete each request.{{{{ if .Prompt }}}}
-
-### Instruction:
-{{{{ .Prompt }}}}
-
-{{{{ end }}}}### Response:
-{{{{ .Response }}}}\"\"\"
-
-PARAMETER stop ""
-PARAMETER stop ""
-PARAMETER stop ""
-PARAMETER stop ""
-PARAMETER stop "<|reserved_special_token_"
-"""
+        model_name = self.config["model_name"].lower()
+        # Mapping from model name keywords to their default TEMPLATE and stop tokens (and optional SYSTEM/num_ctx)
+        mapping = {
+            "llama": {
+                "template": """<|start_header_id|>system<|end_header_id|>
+    Cutting Knowledge Date: December 2023
+    {{ if .System }}{{ .System }}
+    {{- end }}
+    {{- if .Tools }}When you receive a tool call response, use the output to format an answer to the orginal user question.
+    You are a helpful assistant with tool calling capabilities.
+    {{- end }}<|eot_id|>
+    {{- range $i, $_ := .Messages }}
+    {{- $last := eq (len (slice $.Messages $i)) 1 }}
+    {{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>
+    {{- if and $.Tools $last }}
+    Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.
+    Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables.
+    {{ range $.Tools }}
+    {{- . }}
+    {{ end }}
+    {{ .Content }}<|eot_id|>
+    {{- else }}
+    {{ .Content }}<|eot_id|>
+    {{- end }}{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+    {{ end }}
+    {{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|>
+    {{- if .ToolCalls }}
+    {{ range .ToolCalls }}
+    {"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }}
+    {{- else }}
+    {{ .Content }}
+    {{- end }}{{ if not $last }}<|eot_id|>{{ end }}
+    {{- else if eq .Role "tool" }}<|start_header_id|>ipython<|end_header_id|>
+    {{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>
+    {{ end }}
+    {{- end }}
+    {{- end }}""",
+                "stop_tokens": ["<|start_header_id|>", "<|end_header_id|>", "<|eot_id|>"]
+            },
+            "qwen": {
+                "template": """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>
+    {{- else if .Messages }}
+    {{- if or .System .Tools }}<|im_start|>system
+    {{- if .System }}
+    {{ .System }}
+    {{- end }}
+    {{- if .Tools }}
+    # Tools
+    You may call one or more functions to assist with the user query.
+    You are provided with function signatures within <tools></tools> XML tags:
+    <tools>
+    {{- range .Tools }}
+    {"type": "function", "function": {{ .Function }}}
+    {{- end }}
+    </tools>
+    For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
+    <tool_call>
+    {"name": <function-name>, "arguments": <args-json-object>}
+    </tool_call>
+    {{- end }}<|im_end|>
+    {{ end }}
+    {{- range $i, $_ := .Messages }}
+    {{- $last := eq (len (slice $.Messages $i)) 1 -}}
+    {{- if eq .Role "user" }}<|im_start|>user
+    {{ .Content }}<|im_end|>
+    {{ else if eq .Role "assistant" }}<|im_start|>assistant
+    {{ if .Content }}{{ .Content }}
+    {{- else if .ToolCalls }}<tool_call>
+    {{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
+    {{ end }}</tool_call>
+    {{- end }}{{ if not $last }}<|im_end|>
+    {{ end }}
+    {{- else if eq .Role "tool" }}<|im_start|>user
+    <tool_response>
+    {{ .Content }}
+    </tool_response><|im_end|>
+    {{ end }}
+    {{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
+    {{ end }}
+    {{- end }}
+    {{- else }}
+    {{- if .System }}<|im_start|>system
+    {{ .System }}<|im_end|>
+    {{ end }}{{ if .Prompt }}<|im_start|>user
+    {{ .Prompt }}<|im_end|>
+    {{ end }}<|im_start|>assistant
+    {{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}""",
+                "system": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
+                "num_ctx": 32768,
+                "stop_tokens": ["<|endoftext|>"]
+            },
+            "mistral": {
+                "template": "[INST] {{ if .System }}{{ .System }} {{ end }}{{ .Prompt }} [/INST]",
+                "stop_tokens": ["[INST]", "[/INST]"]
+            },
+            "phi": {
+                "template": """{{- range $i, $_ := .Messages }}
+    {{- $last := eq (len (slice $.Messages $i)) 1 -}}
+    <|im_start|>{{ .Role }}<|im_sep|>
+    {{ .Content }}{{ if not $last }}<|im_end|>
+    {{ end }}
+    {{- if and (ne .Role "assistant") $last }}<|im_end|>
+    <|im_start|>assistant<|im_sep|>
+    {{ end }}
+    {{- end }}""",
+                "stop_tokens": ["<|im_start|>", "<|im_end|>", "<|im_sep|>"]
+            },
+            "deepseek": {
+                "template": """{{- if .System }}{{ .System }}{{ end }}
+    {{- range $i, $_ := .Messages }}
+    {{- $last := eq (len (slice $.Messages $i)) 1}}
+    {{- if eq .Role "user" }}<｜User｜>{{ .Content }}
+    {{- else if eq .Role "assistant" }}<｜Assistant｜>{{ .Content }}{{- if not $last }}<｜end▁of▁sentence｜>{{- end }}
+    {{- end }}
+    {{- if and $last (ne .Role "assistant") }}<｜Assistant｜>{{- end }}
+    {{- end }}""",
+                "stop_tokens": ["<｜begin▁of▁sentence｜>", "<｜end▁of▁sentence｜>", "<｜User｜>", "<｜Assistant｜>"]
+            },
+            "llava": {
+                "template": """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>
+    {{- else if .Messages }}
+    {{- if or .System .Tools }}<|im_start|>system
+    {{- if .System }}
+    {{ .System }}
+    {{- end }}
+    {{- if .Tools }}
+    # Tools
+    You may call one or more functions to assist with the user query.
+    You are provided with function signatures within <tools></tools> XML tags:
+    <tools>
+    {{- range .Tools }}
+    {"type": "function", "function": {{ .Function }}}
+    {{- end }}
+    </tools>
+    For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
+    <tool_call>
+    {"name": <function-name>, "arguments": <args-json-object>}
+    </tool_call>
+    {{- end }}<|im_end|>
+    {{ end }}
+    {{- range $i, $_ := .Messages }}
+    {{- $last := eq (len (slice $.Messages $i)) 1 -}}
+    {{- if eq .Role "user" }}<|im_start|>user
+    {{ .Content }}<|im_end|>
+    {{ else if eq .Role "assistant" }}<|im_start|>assistant
+    {{ if .Content }}{{ .Content }}
+    {{- else if .ToolCalls }}<tool_call>
+    {{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
+    {{ end }}</tool_call>
+    {{- end }}{{ if not $last }}<|im_end|>
+    {{ end }}
+    {{- else if eq .Role "tool" }}<|im_start|>user
+    <tool_response>
+    {{ .Content }}
+    </tool_response><|im_end|>
+    {{ end }}
+    {{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
+    {{ end }}
+    {{- end }}
+    {{- else }}
+    {{- if .System }}<|im_start|>system
+    {{ .System }}<|im_end|>
+    {{ end }}{{ if .Prompt }}<|im_start|>user
+    {{ .Prompt }}<|im_end|>
+    {{ end }}<|im_start|>assistant
+    {{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}""",
+                "stop_tokens": ["</s>", "USER:", "ASSSISTANT:"]
+            }
+        }
+        # Select mapping by checking if any key is in the model_name.
+        chosen = None
+        for key, settings in mapping.items():
+            if key in model_name:
+                chosen = settings
+                break
+        if chosen is None:
+            # Fallback default
+            chosen = {
+                "template": """{{ if .System }}<|start_header_id|>system<|end_header_id|>
+    {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
+    {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
+    {{ .Response }}<|eot_id|>""",
+                "stop_tokens": ["<|start_header_id|>", "<|end_header_id|>", "<|eot_id|>"]
+            }
+        # Build the stop parameter lines.
+        stop_params = "\n".join([f"PARAMETER stop {token}" for token in chosen["stop_tokens"]])
+        # Optionally include a SYSTEM line and num_ctx if defined in the mapping.
+        system_line = ""
+        if "system" in chosen:
+            system_line = f"SYSTEM {chosen['system']}\n"
+        num_ctx_line = ""
+        if "num_ctx" in chosen:
+            num_ctx_line = f"PARAMETER num_ctx {chosen['num_ctx']}\n"
+        # Assemble and return the modelfile content.
+        return f"""FROM {output_model}
+    TEMPLATE \"\"\"{chosen['template']}\"\"\"
+    {system_line}{num_ctx_line}{stop_params}
+    """

    def create_and_push_ollama_model(self):
        modelfile_content = self.prepare_modelfile_content()
@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.64"
+version = "2.0.73"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -84,7 +84,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]

 [tool.poetry]
 name = "PraisonAI"
-version = "2.0.64"
+version = "2.0.73"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -3060,7 +3060,7 @@ wheels = [

 [[package]]
 name = "praisonai"
-version = "2.0.64"
+version = "2.0.73"
 source = { editable = "." }
 dependencies = [
    { name = "instructor" },
Author	SHA1	Message	Date
Mervin Praison	df735aa748	Merge pull request #378 from MervinPraison/develop Enhance train.py with Configurable Training Parameters	2025-02-06 07:48:00 +00:00
MervinPraison	3bdf5b6c65	Enhance train.py with Configurable Training Parameters - Updated version to 2.0.73 across multiple files - Refactored TrainModel to use configurable training parameters with default fallbacks - Improved flexibility of training configuration by allowing custom settings via config - Dynamically set training arguments based on provided configuration	2025-02-06 07:47:40 +00:00
Mervin Praison	361fa67108	Merge pull request #377 from MervinPraison/develop Bump version to 2.0.72 and enhance CLI configuration in train and cli…	2025-02-06 07:26:59 +00:00
MervinPraison	c5abcf362a	Bump version to 2.0.72 and enhance CLI configuration in train and cli modules - Updated version to 2.0.72 across multiple files (praisonai.rb, pyproject.toml, uv.lock, Dockerfile, docs) - Refined CLI configuration handling in cli.py for config.yaml generation - Simplified train.py main function to always use provided config path - Improved configuration flexibility for training and model setup	2025-02-06 07:26:41 +00:00
Mervin Praison	7ed2d99a11	Merge pull request #376 from MervinPraison/develop Enhance train.py CLI configuration handling and bump version to 2.0.71	2025-02-06 07:19:11 +00:00
MervinPraison	81307da7c8	Enhance train.py CLI configuration handling and bump version to 2.0.71 - Updated version to 2.0.71 across multiple files (praisonai.rb, pyproject.toml, uv.lock, Dockerfile, docs) - Modified train.py to use existing config.yaml when no CLI arguments are provided - Improved CLI configuration flexibility for training command	2025-02-06 07:18:56 +00:00
Mervin Praison	fb75153b5c	Merge pull request #373 from MervinPraison/develop Bump version to 2.0.70 and update configuration handling	2025-02-05 06:28:07 +00:00
MervinPraison	cddcfcf170	Bump version to 2.0.70 and update configuration handling - Updated version to 2.0.70 across multiple files (praisonai.rb, pyproject.toml, uv.lock, Dockerfile, docs) - Enhanced CLI configuration handling in cli.py to load existing config when not creating a new one - Minor improvements to configuration management and deployment scripts	2025-02-05 06:27:50 +00:00
Mervin Praison	398b0473e0	Merge pull request #372 from MervinPraison/develop Update train.mdx with Wandb configuration and requirements	2025-02-05 05:07:23 +00:00
MervinPraison	c5d260782a	Update train.mdx with Wandb configuration and requirements - Added detailed requirements for training, including Huggingface token and model details - Included Wandb login and configuration instructions - Updated documentation with environment variables for Wandb logging	2025-02-05 05:07:08 +00:00
Mervin Praison	116ac5a64d	Merge pull request #370 from MervinPraison/develop Add Wandb Support for Training and Bump Version to 2.0.69	2025-02-04 11:18:42 +00:00
MervinPraison	abf6e2e8d1	Add Wandb Support for Training and Bump Version to 2.0.69 - Updated version to 2.0.69 across multiple files (praisonai.rb, pyproject.toml, uv.lock, Dockerfile, docs) - Enhanced train.py with optional Wandb logging support - Added environment variable-based Wandb configuration - Implemented dynamic Wandb reporting and run naming	2025-02-04 11:18:20 +00:00
Mervin Praison	cb2b3617fa	Merge pull request #369 from MervinPraison/develop Bump version to 2.0.68 and enhance Ollama model template generation	2025-02-04 11:10:50 +00:00
MervinPraison	6de67c7d0d	Bump version to 2.0.68 and enhance Ollama model template generation - Updated version across multiple files (praisonai.rb, pyproject.toml, uv.lock, Dockerfile, docs) - Significantly improved train.py's prepare_modelfile_content() method - Added comprehensive template and stop token mapping for multiple model architectures - Implemented dynamic template selection based on model name - Enhanced Ollama model file generation with more flexible configuration	2025-02-04 11:10:26 +00:00
Mervin Praison	5699d90894	Merge pull request #368 from MervinPraison/develop Enhance train.py with Dynamic Stop Token Generation for Ollama Models	2025-02-04 10:33:19 +00:00
MervinPraison	2982e8ea9c	Enhance train.py with Dynamic Stop Token Generation for Ollama Models - Added intelligent stop token inference for various model architectures in train.py - Implemented dynamic stop token selection based on model name - Supported models include Phi, LLaVA, Mistral, Qwen, DeepSeek, and default models - Updated Modelfile generation to include model-specific stop tokens - Improved Ollama model preparation flexibility	2025-02-04 10:33:05 +00:00
Mervin Praison	9dd2146342	Merge pull request #367 from MervinPraison/develop Bump version to 2.0.66 and remove TRAIN_AVAILABLE flag	2025-02-04 09:31:20 +00:00
MervinPraison	f79184a6ee	Bump version to 2.0.66 and remove TRAIN_AVAILABLE flag - Updated version across multiple files (praisonai.rb, pyproject.toml, uv.lock, Dockerfile, deploy.py, docs) - Removed TRAIN_AVAILABLE flag from cli.py - Simplified CLI configuration by removing optional training feature check	2025-02-04 09:31:07 +00:00
Mervin Praison	0985e12ad4	Merge pull request #366 from MervinPraison/develop Bump version to 2.0.65 and update deployment configurations	2025-02-04 08:05:06 +00:00
MervinPraison	0fbb7972e1	Bump version to 2.0.65 and update deployment configurations - Updated version across multiple files (praisonai.rb, pyproject.toml, uv.lock, Dockerfile, deploy.py, docs) - Enhanced Conda environment setup script with Ollama installation check - Minor improvements to deployment and setup scripts	2025-02-04 08:04:44 +00:00