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Model: MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned-merged Source: Original Platform
2026-07-17 00:14:10 +08:00
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--- a/README.md
+++ b/README.md
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+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+base_model_relation: finetune
+library_name: transformers
+pipeline_tag: text-generation
+language:
+  - en
+tags:
+  - medical
+  - healthcare
+  - clinical
+  - clinical-decision-support
+  - question-answering
+  - medical-qa
+  - clinical-ner
+  - llama
+  - llama-3.1
+  - qlora
+  - parameter-efficient-fine-tuning
+  - merged
+datasets:
+  - MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset
+  - medalpaca/medical_meadow_medqa
+  - medalpaca/medical_meadow_medical_flashcards
+  - medalpaca/medical_meadow_wikidoc
+  - medalpaca/medical_meadow_wikidoc_patient_information
+  - medalpaca/medical_meadow_cord19
+  - medalpaca/medical_meadow_pubmed_causal
+  - openlifescienceai/medmcqa
+  - bigbio/med_qa
+  - qiaojin/PubMedQA
+  - deepset/covid_qa_deepset
+---
+
+# Med-LLaMA3.1-8B — Medical (merged, standalone)
+
+> A full, ready-to-use medical model: **Llama-3.1-8B** adapted to the medical domain with **QLoRA**, with
+> the LoRA weights **merged back into the base**. Load it directly with `transformers` — no adapter, no
+> PEFT, no extra steps. For the lightweight LoRA-adapter version (apply on top of the base yourself), see
+> the link below.
+
+This is the **8B (high-capacity flagship)** member of the **Med-LLaMA3** family introduced in the paper
+*“Med-LLaMA3: Advancing Medical Question-Answering Through Parameter-Efficient Fine-Tuning of Large
+Language Models”* (Applied Sciences, 2026). The family adapts the LLaMA-3 architecture to the medical
+domain by training only a small fraction of the base model’s parameters (**4.01% for this 8B variant**),
+achieving strong medical question-answering performance while keeping the memory footprint low — enabling
+development and inference on low-cost, consumer-grade hardware.
+
+The 8B variant is the **high-capacity model for complex clinical reasoning**. It attains a **mean
+accuracy of 75.71%** across the eight MMLU medical subsets and performs **comparably to the
+institutionally trained LLaMA3-Med42-8B** at the same scale, while being trained on consumer-grade GPUs.
+
+- 📄 **Paper:** [Med-LLaMA3 (Applied Sciences 2026, 16(12), 6158)](https://www.mdpi.com/2076-3417/16/12/6158) · DOI: [10.3390/app16126158](https://doi.org/10.3390/app16126158)
+- 💻 **Code:** [github.com/Mohamed-Ahmed-Abo-El-Enen/MasterPapers](https://github.com/Mohamed-Ahmed-Abo-El-Enen/MasterPapers)
+- 🧩 **LoRA-adapter version:** [`MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned`](https://huggingface.co/MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned)
+
+---
+
+## Model details
+
+| | |
+|---|---|
+| **This model** | Standalone, merged checkpoint (base + medical LoRA, fused) |
+| **Base model** | [`meta-llama/Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) |
+| **How it was made** | QLoRA fine-tuning (4-bit NF4 base + LoRA, `r=128`, `α=256`, all linear layers), then `merge_and_unload()` into the base |
+| **Trainable parameters (fine-tuning)** | 335.54 M = **4.01%** of the 8.36 B total (base frozen during training) |
+| **Released weights** | Full precision (fp16/bf16; not quantized) |
+| **Parameters** | ~8.03 B |
+| **Architecture** | 32 decoder layers · hidden size 4096 · intermediate size 14,336 · GQA (32 query heads, 8 KV heads) |
+| **Context window** | 128K tokens |
+| **Vocabulary** | 128,256 tokens |
+| **Language** | English |
+| **License** | [Llama 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) |
+
+> **Adapter vs. merged.** This repo is the **merged** model — the medical LoRA is already fused into the
+> weights, so you load it like any standard causal-LM. If you instead want the small (~MB) adapter to
+> apply on top of `meta-llama/Llama-3.1-8B-Instruct` yourself, use the
+> [adapter repo](https://huggingface.co/MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned). Both
+> produce identical outputs. Note: the 8B uses LLaMA **3.1**, not 3.2.
+
+---
+
+## Intended uses
+
+**Primary use cases**
+
+- Medical **question answering** (multiple-choice and open-ended) — the highest-accuracy variant in the family.
+- Clinical knowledge lookup and **clinical decision support** assistance.
+- **Clinical named-entity recognition** (Disease / Procedure extraction) — see results below.
+- Clinical documentation assistance and literature synthesis (with physician review).
+- A research baseline for parameter-efficient fine-tuning of LLaMA models in healthcare.
+
+**Out of scope / not intended for**
+
+- Autonomous clinical decision-making or direct patient care without a qualified clinician in the loop.
+- Generating definitive diagnoses, prescriptions, or treatment plans.
+- Use as a substitute for professional medical advice, emergency services, or licensed care.
+
+See **[Limitations & responsible use](#limitations--responsible-use)** before any applied use.
+
+---
+
+## How to use
+
+This is a standalone model — load it directly, no adapter step required.
+
+```bash
+pip install -U transformers accelerate torch
+```
+
+### Quick start (`pipeline`)
+
+```python
+import torch
+from transformers import pipeline
+
+MODEL = "MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned-merged"
+
+pipe = pipeline("text-generation", model=MODEL, torch_dtype="auto", device_map="auto")
+
+messages = [
+    {"role": "system", "content": "You are a knowledgeable medical assistant. Answer accurately and concisely."},
+    {"role": "user", "content": "What is the first-line treatment for uncomplicated community-acquired pneumonia in a healthy adult?"},
+]
+out = pipe(messages, max_new_tokens=256, do_sample=False)
+print(out[0]["generated_text"][-1]["content"])
+```
+
+### Full control (`AutoModelForCausalLM`)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL = "MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned-merged"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype="auto", device_map="auto")
+model.eval()
+
+messages = [
+    {"role": "system", "content": "You are a knowledgeable medical assistant. Answer accurately and concisely."},
+    {"role": "user", "content": "Explain the mechanism of action of metformin."},
+]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    out = model.generate(inputs, max_new_tokens=256, do_sample=False, temperature=0.0)
+print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+### Low-memory 4-bit inference (recommended; ~5 GB GPU memory)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+# pip install -U bitsandbytes
+
+MODEL = "MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned-merged"
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForCausalLM.from_pretrained(MODEL, quantization_config=bnb_config, device_map="auto")
+```
+
+---
+
+## Training data
+
+The Med-LLaMA3 family was fine-tuned on a curated **medical instruction dataset of over 1.5 million
+samples**, organized along a three-axis taxonomy: **source type** (examination QA, clinical dialogue,
+biomedical literature, encyclopedic reference) × **clinical granularity** (basic science, clinical
+reasoning, patient communication) × **task format** (multiple-choice, open-ended QA, generative
+dialogue). All sources were consolidated into a unified instruction–response schema
+(`system`, `context`, `question`, `answer`, `choices`).
+
+Sources include:
+
+- **MedAlpaca / Medical Meadow** collection — MEDIQA, Medical Flashcards, WikiDoc, WikiDoc Patient
+  Information, MedQA, CORD-19, and PubMed Causal subsets
+- **MedMCQA** — Indian medical entrance exam (AIIMS & NEET PG) multiple-choice questions
+- **MedQA-USMLE** — USMLE-style 4-option multiple-choice questions (English)
+- **BigBIO MedQA** — standardized biomedical QA
+- **PubMedQA** — research questions over PubMed abstracts (yes/no/maybe)
+- **COVID-QA (deepset)** — COVID-19 / SARS-CoV-2 question answering
+- **MedQuAD** — consumer-health QA compiled from authoritative NIH sources
+- **HealthCareMagic** — real-world patient–doctor conversation transcripts
+
+The data-cleaning and corpus-assembly scripts are released in the
+[code repository](https://github.com/Mohamed-Ahmed-Abo-El-Enen/MasterPapers), and the final compiled
+fine-tuning dataset is available at
+[`MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset`](https://huggingface.co/datasets/MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset).
+
+> **Evaluation integrity:** The eight **MMLU medical subsets** were used **only for held-out
+> evaluation** and were **excluded** from the fine-tuning corpus. For benchmarks with official splits
+> (MedMCQA, MedQA-USMLE, PubMedQA), only the official **training** partitions were used for fine-tuning.
+
+---
+
+## Training procedure
+
+This model was produced by QLoRA fine-tuning followed by merging the adapter into the base. LoRA and
+optimization settings are identical across the 1B, 3B, and 8B variants; sequence length, batch size, and
+gradient accumulation are scaled to each model’s memory footprint. The settings below are for the **8B**
+variant.
+
+| Setting | Value (8B) |
+|---|---|
+| Method | QLoRA (4-bit NF4 base, LoRA adapters in higher precision) → merged into base |
+| LoRA `r` / `α` / dropout / bias | 128 / 256 / 0.05 / none |
+| Target modules | All linear layers (q, k, v, o, gate, up, down) |
+| Trainable params | 335.54 M (4.01% of 8.36 B) |
+| Quantization (training) | 4-bit NF4 with double quantization (bitsandbytes) |
+| Optimizer | Paged AdamW 8-bit (β₁ = 0.9, β₂ = 0.999), weight decay 0.1 |
+| Learning rate / schedule | 2.0 × 10⁻⁵ / cosine annealing, 5 warmup steps |
+| Epochs | 5 |
+| Max sequence length | 4096 |
+| Batch size / grad accumulation | 1 per device / 4 steps |
+| Max gradient norm | 1.0 |
+| Precision & memory | float16 · gradient checkpointing (no DeepSpeed / FlashAttention — unsupported on T4) |
+| Hardware | 2 × NVIDIA T4 (30 GB total, free-tier Kaggle); ~15–25 days per run |
+| Experiment tracking | Weights & Biases |
+
+---
+
+## Evaluation
+
+All re-run models (including the baselines) were evaluated with the **EleutherAI LM Evaluation Harness**
+under identical conditions: same harness version (v0.4.2), identical prompt templates, and **5-shot**
+prompting. Reported `±` values are **95% bootstrap confidence intervals** (1000 resamples); statistical
+significance uses **McNemar’s test** on per-item paired correctness. The merged model is functionally
+identical to the base + adapter, so all scores below apply to both.
+
+### MMLU medical subsets (5-shot accuracy %)
+
+Comparison against the institutionally trained **LLaMA3-Med42-8B** under identical conditions
+(this is the comparison visualized in Figure 3 of the paper). Family context: mean accuracy scales with
+size — **1B = 48.64%**, **3B = 64.24%**, **8B = 75.71%**.
+
+| MMLU medical subset | Med-LLaMA3.1-8B (Ours) | LLaMA3-Med42-8B |
+|---|---|---|
+| Anatomy | **71.11** (±3.92) | 69.63 (±3.97) |
+| Clinical Knowledge | **79.62** (±2.48) | 76.60 (±2.61) |
+| College Biology | **84.03** (±3.06) | 81.25 (±3.26) |
+| College Medicine | **67.63** (±3.57) | 67.05 (±3.58) |
+| Medical Genetics | **84.00** (±3.68) | 76.00 (±4.29) |
+| Nutrition | **83.66** (±2.12) | 72.88 (±2.55) |
+| Professional Medicine | **77.21** (±2.55) | 75.00 (±2.63) |
+| Virology | **58.43** (±3.84) | 49.40 (±3.89) |
+| **Mean (8 subsets)** | **75.71** | 71.10 |
+
+**Statistical interpretation (honest framing):**
+- vs. **`Llama-3.1-8B-Instruct`** (untuned base): improvements are statistically significant on Anatomy
+  (p=0.021), Clinical Knowledge (p=0.003), Medical Genetics (p<0.001), Nutrition (p<0.001), and
+  Professional Medicine (p=0.008); College Biology (p=0.11) and College Medicine (p=0.43) are not
+  significant. After **Bonferroni correction** across the eight subsets (per-subset threshold 0.00625),
+  Clinical Knowledge, Medical Genetics, and Nutrition remain significant; Anatomy and Professional
+  Medicine are significant only before correction.
+- vs. **`LLaMA3-Med42-8B`** (institutionally trained): differences are **not statistically significant**
+  — i.e., **comparable performance**, not demonstrated superiority, at the same parameter scale.
+
+### Medical QA benchmarks (5-shot accuracy %)
+
+| Model | MedMCQA | MedQA | PubMedQA |
+|---|---|---|---|
+| **Med-LLaMA3.1-8B (Ours)** | **61.8** (±0.75) | 62.4 (±1.37) | **77.0** (±1.96) |
+| LLaMA3-Med42-8B | 60.3 (±0.76) | **62.8** (±1.36) | **77.0** (±1.88) |
+| Llama-3.1-8B-Instruct | 59.6 (±0.76) | 61.9 (±1.36) | 75.8 (±1.92) |
+| MedGemma-4B-it | 32.2 (±0.72) | 27.7 (±1.26) | 55.2 (±2.23) |
+
+The **+2.2-point gain on MedMCQA over `Llama-3.1-8B-Instruct` is statistically significant** (p=0.004).
+The difference vs. `LLaMA3-Med42-8B` on MedMCQA is not significant (p=0.38). On MedQA and PubMedQA the
+models are statistically tied.
+
+### Clinical named-entity recognition (zero-shot, 100 MIMIC-III discharge summaries)
+
+Dual-annotator gold standard, inter-rater agreement **κ = 0.87**.
+
+| Entity type | Precision | Recall | F1 | Support |
+|---|---|---|---|---|
+| Disease | 0.94 | 0.91 | 0.92 | 847 |
+| Procedure | 0.97 | 0.93 | 0.95 | 463 |
+| **Macro avg** | **0.96** | **0.92** | **0.94** | 1310 |
+
+### Efficiency (4-bit inference)
+
+Approximately **3.58 GB** model size and **~5.2 GB** GPU memory allocated at inference, ~**30 tokens/s**,
+with ~20 ms first-token latency — comparable to other 8B 4-bit baselines and well within consumer-GPU
+budgets.
+
+See the [paper](https://www.mdpi.com/2076-3417/16/12/6158) for full tables, generation-quality metrics,
+expert evaluation, and the safety analysis.
+
+---
+
+## Limitations & responsible use
+
+- **Not a medical device.** This model is a research artifact. It must **not** be used for autonomous
+  diagnosis, treatment, prescribing, or any decision affecting patient care without review by a
+  qualified healthcare professional. In expert review of 100 generative cases, 94% were rated “Good” but
+  **6% contained critical errors** — underscoring the need for human verification.
+- **Comparable, not superior.** Against the institutionally trained `LLaMA3-Med42-8B`, differences are
+  not statistically significant; gains over the untuned base are significant on some subsets but not all
+  (see above). On MedQA and PubMedQA there is no significant advantage.
+- **Hallucination & over-elaboration.** The model can produce fluent but incorrect information and tends
+  to elaborate beyond the question, which may obscure key points in time-sensitive settings.
+- **Abbreviation ambiguity.** A known high-severity error source. The paper’s safety pilot shows that
+  **context-disambiguation preprocessing** reduces abbreviation-ambiguity errors from 30% to 10% on a
+  held-out set; consider applying similar preprocessing.
+- **Data & bias.** Training data may under-represent certain populations, conditions, or regional
+  practices, and may encode biases present in the source corpora. Rare-condition coverage is limited.
+- **Privacy & compliance.** Do not input protected health information (PHI) unless your deployment is
+  appropriately secured and compliant with applicable regulations (e.g., HIPAA, GDPR).
+- **Evaluation scope.** Benchmarks are English-only and dominated by multiple-choice formats; long-form
+  reasoning, multi-turn dialogue safety, non-English text, and out-of-distribution robustness are not
+  evaluated.
+
+**Recommended deployment:** clinical decision *support* (not autonomous decisions), educational tool,
+documentation assistant (with physician review), and literature synthesis.
+
+---
+
+## License
+
+This model is released under the **[Llama 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)**,
+inherited from the base model. By using it you agree to Meta’s Llama 3.1 license terms and
+Acceptable Use Policy. Review the licenses of the individual training datasets for any additional
+restrictions on derived use.
+
+---
+
+## Citation
+
+If you use this model, please cite the paper:
+
+```bibtex
+@article{aboelenen2026medllama3,
+  title   = {Med-LLaMA3: Advancing Medical Question-Answering Through Parameter-Efficient Fine-Tuning of Large Language Models},
+  author  = {Abo El-Enen, Mohamed Ahmed and Ismail, Sally S. and Nazmy, Taymoor Mohamed},
+  journal = {Applied Sciences},
+  volume  = {16},
+  number  = {12},
+  pages   = {6158},
+  year    = {2026},
+  publisher = {MDPI},
+  doi     = {10.3390/app16126158},
+  url     = {https://www.mdpi.com/2076-3417/16/12/6158}
+}
+```
+
+## Authors & contact
+
+Mohamed Ahmed Abo El-Enen, Sally S. Ismail, and Taymoor Mohamed Nazmy
+Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
+
+---
+
+## Model family
+
+| Variant | Type | Repository |
+|---|---|---|
+| Med-LLaMA3.2-1B | Adapter | [`MohamedAhmedAE/Llama-3.2-1B-Instruct-Medical-Finetuned`](https://huggingface.co/MohamedAhmedAE/Llama-3.2-1B-Instruct-Medical-Finetuned) |
+| Med-LLaMA3.2-1B | Merged | [`MohamedAhmedAE/Llama-3.2-1B-Instruct-Medical-Finetuned-merged`](https://huggingface.co/MohamedAhmedAE/Llama-3.2-1B-Instruct-Medical-Finetuned-merged) |
+| Med-LLaMA3.2-3B | Adapter | [`MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned`](https://huggingface.co/MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned) |
+| Med-LLaMA3.2-3B | Merged | [`MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged`](https://huggingface.co/MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged) |
+| Med-LLaMA3.1-8B | Adapter | [`MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned`](https://huggingface.co/MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned) |
+| **Med-LLaMA3.1-8B** | **Merged** | **this repo** — [`MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned-merged`](https://huggingface.co/MohamedAhmedAE/Llama-3.1-8B-Instruct-Medical-Finetuned-merged) |
+
+**Fine-tuning dataset:** [`MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset`](https://huggingface.co/datasets/MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset)
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,109 @@
+{{- 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 Jul 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']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\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 %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|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|>\n\n' -}}
+            {{- '{"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|>\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 %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,40 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "factor": 8.0,
+    "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": false,
+  "transformers_version": "5.10.1",
+  "use_cache": true,
+  "vocab_size": 128256
+}
--- a/generation_config.json
+++ b/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": "5.10.1"
+}
--- a/model.safetensors
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f151735455f64039f4c8202fc9c71a970831aecd786a47bf32ecaeac733fd6b
+size 16060556616
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,14 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": false,
+  "local_files_only": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "TokenizersBackend"
+}