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Model: Harsh-k-007/fitcoach-3b
Source: Original Platform

.gitattributes

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README.md

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+---
+license: llama3.2
+base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
+library_name: transformers
+tags:
+- sft
+- trl
+- unsloth
+- fitness
+- nutrition
+- coaching
+- conversational
+- merged
+datasets:
+- Harsh-k-007/fitcoach-conversations
+pipeline_tag: text-generation
+---
+
+# FitCoach 3B (Merged, fp16)
+
+A fully merged, full-precision (fp16) fine-tune of Llama 3.2 3B Instruct, acting as
+**FitCoach** — a conversational fitness and nutrition intake coach. This is the
+lightweight option in the FitCoach model family, alongside the
+[8B LoRA adapter](https://huggingface.co/Harsh-k-007/fitcoach-8b-adapter).
+
+Try it live: [FitCoach Space](https://huggingface.co/spaces/Harsh-k-007/fitcoach)
+
+## Model Details
+
+- **Base model:** `unsloth/Llama-3.2-3B-Instruct-bnb-4bit` (loaded in 4-bit for
+  training via Unsloth, then merged to 16-bit for deployment)
+- **Format:** merged weights, fp16 — no adapter required, load directly
+- **Adapter (during training):** LoRA, rank 16, alpha 16, dropout 0, targeting all
+  attention and MLP projection layers (`q/k/v/o_proj`, `gate/up/down_proj`)
+- **Training framework:** Unsloth `FastLanguageModel` + TRL `SFTTrainer`
+- **Training data:** [`Harsh-k-007/fitcoach-conversations`](https://huggingface.co/datasets/Harsh-k-007/fitcoach-conversations)
+  — 1,407 synthetic coaching conversations (95/5 train/eval split for this run)
+- **Sequence length:** 2048 tokens, with sequence packing (`bfd` strategy)
+- **Precision:** bf16 training on a single T4 GPU (Google Colab free tier)
+- **Epochs:** 2, effective batch size 8 (2 × 4 grad accumulation), cosine LR
+  schedule, peak LR 2e-4
+
+## Intended Use
+
+FitCoach is a **conversational intake coach** for fitness and nutrition. Given a
+user's goal, it asks **one question at a time** to gather the relevant context, then
+generates a structured plan.
+
+Scope is intentionally narrow:
+- **Meal plans** (~60% of training data): collects goal, age/height/weight, dietary
+  restrictions, activity level
+- **Workout plans** (~40% of training data): collects goal, experience level, days
+  per week, equipment access
+
+The 3B model is intended as a **lighter, faster** alternative to the 8B adapter —
+useful where latency or memory matters more than maximum response quality.
+
+### Out of scope
+
+- Injuries, medical conditions, or any medical advice
+- Macro/calorie arithmetic — the model can *describe* macro targets conceptually but
+  is **not reliable at computing them**; treat any numeric macro breakdown as
+  approximate, not verified
+- Unprompted macro generation — the model does not currently generate macros unless
+  explicitly asked (known dataset gap, planned for v2)
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "Harsh-k-007/fitcoach-3b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer.pad_token = "<|finetune_right_pad_id|>"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    dtype=torch.float16,
+    device_map="auto",
+)
+model.eval()
+
+messages = [
+    {"role": "system", "content": "You are FitCoach, a friendly fitness and nutrition coach."},
+    {"role": "user", "content": "Create a simple fat-loss meal plan with Indian food options."},
+]
+
+encoded = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
+input_ids = encoded["input_ids"] if hasattr(encoded, "keys") else encoded
+input_ids = input_ids.to(model.device)
+
+output = model.generate(
+    input_ids,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    pad_token_id=128004,
+)
+print(tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True))
+```
+
+## Training Procedure
+
+- **Method:** Supervised fine-tuning (SFT) with TRL `SFTTrainer`, using Unsloth's
+  `FastLanguageModel` for memory-efficient LoRA training (gradient checkpointing via
+  `use_gradient_checkpointing="unsloth"`), then merged to full precision via
+  `save_pretrained_merged(..., save_method="merged_16bit")`
+- **Loss:** full-conversation loss (`train_on_responses_only` / assistant-only
+  masking was not applied in this run — a documented future optimization once
+  reliably supported for the Llama 3 chat template)
+- **Chat template:** Llama 3.2 (`unsloth.chat_templates.get_chat_template`)
+- **Optimizer:** `adamw_8bit`, weight decay 0.01, cosine schedule, 17 warmup steps
+- **Hardware:** Google Colab T4 (free tier), with Drive checkpointing for
+  resumability across the 90-minute idle / 12-hour session limits
+
+## Known Limitations
+
+- **Macro arithmetic is hallucinated.** The model isn't reliable at computing
+  calorie/macro numbers. A v2 release plans to add a calculator/tool layer for this.
+- **Macros aren't generated unprompted.** The dataset under-represents this, so the
+  model needs to be asked explicitly. Planned fix for v2 via dataset augmentation.
+- **No assistant-only loss** in this training run (see above).
+- As the smaller model in the family, expect slightly less consistent intake
+  behavior and plan structure compared to the 8B adapter.
+
+## Citation
+
+If you use this model, please link back to this repo and the
+[training dataset](https://huggingface.co/datasets/Harsh-k-007/fitcoach-conversations).

chat_template.jinja

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+{{- 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 %}
+    {%- set date_string = "26 July 2024" %}
+{%- 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'] %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>
+
+" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython
+" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "
+
+"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023
+" }}
+{{- "Today Date: " + date_string + "
+
+" }}
+{%- 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.
+
+" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "
+
+" }}
+    {%- 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'] %}
+        {%- 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|>
+
+' -}}
+    {{- "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.
+
+" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "
+
+" }}
+    {%- 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|>
+
+'+ message['content'] + '<|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 %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>
+
+' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>
+
+' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>
+
+" }}
+        {%- 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|>
+
+' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "torch_dtype": "float16",
+    "eos_token_id": 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,
+    "pad_token_id": 128004,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+        "factor": 32.0,
+        "high_freq_factor": 4.0,
+        "low_freq_factor": 1.0,
+        "original_max_position_embeddings": 8192,
+        "rope_theta": 500000.0,
+        "rope_type": "llama3"
+    },
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.5.10",
+    "use_cache": false,
+    "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.5.0"
+}

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