初始化项目，由ModelHub XC社区提供模型

Model: MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged
Source: Original Platform

.gitattributes

@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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

README.md

@@ -0,0 +1,329 @@
+---
+license: llama3.2
+base_model: meta-llama/Llama-3.2-3B-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
+  - llama
+  - llama-3.2
+  - 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-LLaMA3.2-3B — Medical (merged, standalone)
+
+> A full, ready-to-use medical model: **Llama-3.2-3B** 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 **3B (balanced / mid-tier)** 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 (**5.70% for this 3B variant**),
+achieving strong medical question-answering performance while keeping the memory footprint low — enabling
+development and inference on low-cost, consumer-grade hardware.
+
+The 3B variant offers a **balanced trade-off between computational efficiency and capacity** — a
+competitive mid-tier option with meaningfully better accuracy than the 1B and lower compute than the 8B.
+
+- 📄 **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-3.2-3B-Instruct-Medical-Finetuned`](https://huggingface.co/MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned)
+
+---
+
+## Model details
+
+| | |
+|---|---|
+| **This model** | Standalone, merged checkpoint (base + medical LoRA, fused) |
+| **Base model** | [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-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)** | 194.51 M = **5.70%** of the 3.40 B total (base frozen during training) |
+| **Released weights** | bfloat16 (full precision; not quantized) |
+| **Parameters** | ~3.21 B |
+| **Architecture** | 28 decoder layers · hidden size 3072 · intermediate size 8192 · GQA (24 query heads, 8 KV heads) |
+| **Context window** | 128K tokens |
+| **Vocabulary** | 128,256 tokens |
+| **Language** | English |
+| **License** | [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/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-3.2-3B-Instruct` yourself, use the
+> [adapter repo](https://huggingface.co/MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned). Both
+> produce identical outputs.
+
+---
+
+## Intended uses
+
+**Primary use cases**
+
+- Medical **question answering** (multiple-choice and open-ended).
+- Clinical knowledge lookup and **clinical decision support** assistance.
+- A balanced mid-tier option when the 1B is too small and the 8B is too heavy.
+- A research baseline for parameter-efficient fine-tuning of small 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-3.2-3B-Instruct-Medical-Finetuned-merged"
+
+pipe = pipeline("text-generation", model=MODEL, torch_dtype=torch.bfloat16, 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-3.2-3B-Instruct-Medical-Finetuned-merged"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, 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
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+# pip install -U bitsandbytes
+
+MODEL = "MohamedAhmedAE/Llama-3.2-3B-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 **3B**
+variant.
+
+| Setting | Value (3B) |
+|---|---|
+| 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 | 194.51 M (5.70% of 3.40 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 | 2048 |
+| Batch size / grad accumulation | 4 per device / 16 steps |
+| Max gradient norm | 1.0 |
+| Precision & memory | bfloat16 · gradient checkpointing · DeepSpeed ZeRO-2 · FlashAttention-2 |
+| Hardware | 2 × NVIDIA RTX 4050 (12 GB), ~30 days |
+| Experiment tracking | Weights & Biases |
+
+---
+
+## Evaluation
+
+Evaluation in the paper uses the **EleutherAI LM Evaluation Harness** with **5-shot** prompting on the
+eight MMLU medical subsets (Anatomy, Clinical Knowledge, College Biology, College Medicine, Medical
+Genetics, Nutrition, Professional Medicine, Virology). Reported comparisons include **McNemar’s test**
+p-values and **95% bootstrap confidence intervals**.
+
+The table below reports the 3B model’s 5-shot accuracy (%) on each MMLU medical subset, with 95%
+bootstrap confidence intervals (1000 resamples), as published in Table 7 of the paper. For context, the
+family’s mean accuracy scales with model size: **1B = 48.64%**, **3B = 64.24%**, **8B = 75.71%**.
+
+| MMLU medical subset (5-shot) | Med-LLaMA3.2-3B (acc. %) |
+|---|---|
+| Anatomy | 59.52 (±4.26) |
+| Clinical Knowledge | 68.17 (±2.89) |
+| College Biology | 71.53 (±3.77) |
+| College Medicine | 57.65 (±3.78) |
+| Medical Genetics | 75.00 (±4.35) |
+| Nutrition | 68.32 (±2.69) |
+| Professional Medicine | 70.59 (±2.77) |
+| Virology | 43.17 (±3.84) |
+| **Mean (8 subsets)** | **64.24** |
+
+> The merged model is functionally identical to the base + adapter, so these scores apply to both. The
+> paper reports an untuned baseline only for the 8B model (vs. `Llama-3.1-8B-Instruct`); it does **not**
+> include an untuned `Llama-3.2-3B` baseline on these subsets. See Table 7 of the paper for the full
+> cross-model comparison (1B, 8B, and other ≤8B models) with statistical tests.
+
+See the [paper](https://www.mdpi.com/2076-3417/16/12/6158) for full tables, statistical tests, and
+confidence intervals.
+
+---
+
+## 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.
+- **Hallucination risk.** Like all LLMs, it can produce fluent but incorrect or fabricated medical
+  information. Always verify outputs against authoritative sources.
+- **Mid-tier capacity.** The 3B is a balanced variant; for the highest accuracy on complex clinical
+  reasoning, prefer the 8B variant when resources allow. For the smallest footprint, the 1B is available.
+- **Abbreviation ambiguity.** Medical abbreviations are a known error source. The paper’s safety pilot
+  shows that **context-disambiguation preprocessing** reduces the highest-severity abbreviation
+  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.
+- **Privacy & compliance.** Do not input protected health information (PHI) unless your deployment is
+  appropriately secured and compliant with applicable regulations (e.g., HIPAA, GDPR).
+- **English only.** Performance outside English is not evaluated.
+
+---
+
+## License
+
+This model is released under the **[Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)**,
+inherited from the base model. By using it you agree to Meta’s Llama 3.2 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** | **this repo** — [`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 | [`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)

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,
+  "dtype": "bfloat16",
+  "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,
+  "transformers_version": "4.57.1",
+  "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": "4.57.1"
+}

model-00001-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13cbd6d16e927a0c5bad54102514e6e18b4a47b3a6eb911e39d678d328d19f55
+size 4965799096

model-00002-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b770216613ac5c34d7c54bdff1fa616bc4e338a9d0b20af6303e48c295ee23c
+size 1459729952

model.safetensors.index.json

@@ -0,0 +1,262 @@
+{
+  "metadata": {
+    "total_parameters": 3212749824,
+    "total_size": 6425499648
+  },
+  "weight_map": {
+    "model.embed_tokens.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.1.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.1.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.1.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.1.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.1.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.1.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.1.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.1.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.10.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.10.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.10.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.10.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.10.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.10.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.10.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.10.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.10.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.11.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.11.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.11.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.11.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.11.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.11.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.11.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.11.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.11.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.12.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.12.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.12.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.12.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.12.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.12.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.12.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.12.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.12.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.13.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.13.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.13.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.13.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.13.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.13.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.13.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.13.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.13.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.14.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.14.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.14.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.14.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.14.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.14.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.14.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.14.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.14.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.15.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.15.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.15.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.15.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.15.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.15.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.15.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.15.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.15.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.16.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.16.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.16.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.16.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.16.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.16.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.16.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.16.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.16.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.17.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.17.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.17.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.17.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.17.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.17.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.17.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.17.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.17.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.18.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.18.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.18.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.18.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.18.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.18.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.18.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.18.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.18.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.19.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.19.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.19.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.19.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.19.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.19.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.19.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.19.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.19.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.2.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.2.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.2.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.2.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.2.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.2.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.2.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.2.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.2.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.20.input_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.20.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.20.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.20.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.20.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.20.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.20.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.20.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.20.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.21.input_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.21.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.21.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.21.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.21.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.21.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.21.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.21.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.21.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.22.input_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.22.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.22.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.22.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.22.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.22.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.22.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.22.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.22.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.23.input_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.23.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.23.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.23.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.23.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.23.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.23.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.23.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.23.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.24.input_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.24.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.24.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.24.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.24.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.24.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.24.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.24.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.24.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.25.input_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.25.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.25.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.25.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.25.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.25.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.25.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.25.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.25.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.26.input_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.26.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.26.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.26.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.26.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.26.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.26.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.26.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.26.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.27.input_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.27.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.27.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.27.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.27.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
+    "model.layers.27.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.27.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.27.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.27.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
+    "model.layers.3.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.3.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.3.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.3.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.3.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.3.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.3.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.3.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.3.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.4.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.4.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.4.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.4.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.4.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.4.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.4.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.4.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.4.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.5.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.5.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.5.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.5.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.5.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.5.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.5.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.5.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.5.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.6.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.6.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.6.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.6.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.6.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.6.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.6.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.6.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.6.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.7.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.7.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.7.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.7.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.7.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.7.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.7.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.8.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.8.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.8.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.8.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.8.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.8.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.norm.weight": "model-00002-of-00002.safetensors"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}