Add Trek Nepal 3B fine-tuned model - Complete model with LoRA adapter and merged weights - Optimized for Nepal trekking information - Based on Llama-3.2-3B-Instruct - Includes Gradio app interface

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Environment files
+.env
+.env.local
+
+# Python cache
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+
+# Development files
+test_*.py
+create_*.py
+debug_*.py
+
+# Gradio cache
+.gradio/
+
+# IDE files
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log

README.md

@@ -1,13 +1,312 @@
 ---
-title: Trek Nepal 3b
-emoji: 🏢
+title: Trek Nepal 3B
+emoji: 🏔️
 colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 5.35.0
-app_file: app.py
-pinned: false
+colorTo: green
+sdk: transformers
+sdk_version: 4.37.0
+base_model: meta-llama/Llama-3.2-3B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+  - peft
+  - lora
+  - nepal
+  - trekking
+  - travel
+  - mountaineering
+  - hiking
+  - llama
+  - llama3.2
+  - sft
+  - transformers
+  - trl
+model_type: llama
+language:
+  - en
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Trek Nepal 3B - Fine-tuned Llama-3.2-3B for Nepal Trekking
+
+This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) specifically trained on Nepal trekking and travel information.
+
+## Model Details
+
+- **Base Model**: meta-llama/Llama-3.2-3B-Instruct
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+- **LoRA Rank**: 256
+- **LoRA Alpha**: 512
+- **Specialization**: Nepal trekking routes, permits, gear, weather, safety, accommodation, costs, and preparation
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+### Framework versions
+
+- PEFT 0.16.0
+
+## Usage
+
+### Using with Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-3B",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B")
+
+# Load the LoRA adapter
+model = PeftModel.from_pretrained(base_model, "your-username/trek-nepal-3b")
+
+# Generate text
+prompt = "What permits do I need for Everest Base Camp trek?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Chat Template
+
+The model uses the Qwen2.5 chat template format:
+
+```
+<|im_start|>system
+You are a helpful assistant specialized in providing information about trekking in Nepal.
+<|im_end|>
+<|im_start|>user
+{user_message}
+<|im_end|>
+<|im_start|>assistant
+```
+
+## Training Details
+
+- **Training Method**: Supervised Fine-Tuning (SFT) with LoRA
+- **Dataset**: Custom Nepal trekking Q&A dataset
+- **Optimization**: 4-bit quantization with bfloat16
+- **GPU**: Optimized for L40S GPU training
+- **Batch Size**: 16 with gradient accumulation steps of 2
+
+## Intended Use
+
+This model is specifically designed for:
+
+- Nepal trekking route information
+- Permit requirements and procedures
+- Gear and equipment recommendations
+- Weather and seasonal advice
+- Safety guidelines and preparations
+- Accommodation and logistics
+- Cost estimation and budgeting
+
+## Limitations
+
+- Specialized only for Nepal trekking topics
+- May not perform well on general conversation topics
+- Information is based on training data and should be verified with official sources
+- Always consult local authorities and guides for current conditions
+
+## License
+
+This model is licensed under Apache 2.0.
+
+## Training Configuration
+
+The model was trained using the following LoRA configuration:
+
+- **r**: 128
+- **lora_alpha**: 256
+- **lora_dropout**: 0.05
+- **target_modules**: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
+- **bias**: "none"
+- **task_type**: "CAUSAL_LM"

app.py

@@ -0,0 +1,103 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Load model and tokenizer
+def load_model():
+    try:
+        logger.info("Loading base model...")
+        base_model = AutoModelForCausalLM.from_pretrained(
+            "meta-llama/Llama-3.2-3B-Instruct",
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            trust_remote_code=True
+        )
+        
+        logger.info("Loading tokenizer...")
+        tokenizer = AutoTokenizer.from_pretrained(
+            "meta-llama/Llama-3.2-3B-Instruct",
+            trust_remote_code=True
+        )
+        
+        logger.info("Loading LoRA adapter...")
+        model = PeftModel.from_pretrained(base_model, ".")
+        
+        return model, tokenizer
+    except Exception as e:
+        logger.error(f"Error loading model: {e}")
+        return None, None
+
+# Initialize model and tokenizer
+model, tokenizer = load_model()
+
+def generate_response(message, history):
+    if model is None or tokenizer is None:
+        return "Error: Model not loaded properly."
+    
+    try:
+        # Create the system prompt
+        system_prompt = "You are a helpful assistant specialized in providing information about trekking in Nepal. Answer questions about Nepal trekking routes, permits, gear, weather, safety, accommodation, costs, and preparation. If asked about topics outside Nepal trekking, politely redirect to Nepal trekking topics."
+        
+        # Format the conversation
+        conversation = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
+        
+        # Tokenize
+        inputs = tokenizer(conversation, return_tensors="pt", truncation=True, max_length=1024)
+        
+        # Generate
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=512,
+                temperature=0.7,
+                top_p=0.9,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id
+            )
+        
+        # Decode response
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract only the assistant's response
+        if "<|im_start|>assistant\n" in response:
+            response = response.split("<|im_start|>assistant\n")[-1]
+        
+        return response
+        
+    except Exception as e:
+        logger.error(f"Error generating response: {e}")
+        return f"Error generating response: {str(e)}"
+
+# Create Gradio interface
+iface = gr.ChatInterface(
+    fn=generate_response,
+    title="🏔️ Trek-Nepal-3B",
+    description="""You are Trek Nepal, a friendly and knowledgeable Nepal trekking expert assistant. You specialize in providing information about trekking in Nepal.
+
+                You can respond to greetings, have casual conversations but within your scope, and be helpful and personable. When it comes to providing information, you focus exclusively on Nepal trekking topics.
+
+                For Nepal trekking questions, provide detailed, accurate answers based on your expertise with proper markdown format.
+
+                For questions completely unrelated to Nepal trekking (like other countries' travel, technology, politics, recipes, or any other field outside of trekking in Nepal), politely redirect: "I'm sorry, I specialize in Nepal trekking information. Is there anything about trekking in Nepal I can help you with?"
+
+                Do not reveal any internal mechanisms or technical details about you. Be natural, friendly, and conversational while maintaining your expertise in Nepal trekking.""",
+    examples=[
+        "What permits do I need for Everest Base Camp trek?",
+        "What gear should I pack for Annapurna Circuit?",
+        "What's the best time to trek in Nepal?",
+        "How much does it cost to trek to Everest Base Camp?",
+        "What are the safety considerations for high altitude trekking?",
+        "Tell me about accommodation options on popular trekking routes."
+    ],
+    theme=gr.themes.Soft(),
+    cache_examples=True
+)
+
+if __name__ == "__main__":
+    iface.launch()

chat_template.jinja

@@ -0,0 +1,93 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,39 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.52.3"
+}

lora/adapter_config.json

@@ -0,0 +1,41 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-3.2-3B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 256,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 128,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "down_proj",
+    "o_proj",
+    "up_proj",
+    "gate_proj",
+    "k_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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requirements.txt

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+torch>=2.0.0
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special_tokens_map.json

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tokenizer.json

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