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Model: kosiasuzu/chatml-llama3.1-8b-lora-merged
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+license: llama3.1
+base_model: kosiasuzu/chatml-agent-llama-3.1-8b-init
+tags:
+- llama-3.1
+- chatml
+- lora
+- agent
+- tool-use
+- agenticml-baseline
+pipeline_tag: text-generation
+---
+
+# kosiasuzu/chatml-llama3.1-8b-lora-merged
+
+ChatML baseline for the AgenticML agent-format study: **Llama 3.1 8B** with instruct-style ChatML templates and tool-call tokens, LoRA-fine-tuned on the same agent-trajectory corpus as the AgenticML-format model, then **merged** into full weights for single-checkpoint inference.
+
+This checkpoint is the **ChatML+tools comparison arm**—same data and training recipe as `kosiasuzu/agenticml-llama3.1-8b-lora-merged`, differing only in serialization (HF `messages` + `apply_chat_template` vs AgenticML frames).
+
+## Model Details
+
+### Model Description
+
+- **Model ID:** `kosiasuzu/chatml-llama3.1-8b-lora-merged`
+- **Developed by:** AgenticML project / kosiasuzu
+- **Model type:** Causal language model (Llama 3.1 8B), **PEFT LoRA merged** into dense weights
+- **Language(s):** English (primary)
+- **License:** [Llama 3.1 Community License](https://llama.meta.com/llama3_1/license/) (inherits from Meta Llama 3.1 base; confirm before commercial use)
+- **Finetuned from:** [`kosiasuzu/chatml-agent-llama-3.1-8b-init`](https://huggingface.co/kosiasuzu/chatml-agent-llama-3.1-8b-init)
+
+**Init base (`chatml-agent-llama-3.1-8b-init`):** `meta-llama/Llama-3.1-8B` weights + `meta-llama/Llama-3.1-8B-Instruct` tokenizer; ChatML special-token rows in `embed_tokens` / `lm_head` mean-pooled from seed words (`agenticml init-embeddings --format chatml`).
+
+**Training target:** supervised next-token prediction on the **`messages`** column (ChatML conversation derived from AgenticML trajectories via `bridge.frames_to_messages` during `data-clean-push`). Labels are masked to **assistant** spans only.
+
+### Model Sources
+
+- **AgenticML repository:** https://github.com/kosiasuzu/agenticml
+- **Dataset:** [`kosiasuzu/agenticml-agent-trajectory-dataset`](https://huggingface.co/datasets/kosiasuzu/agenticml-agent-trajectory-dataset)
+- **Paired AgenticML merged model:** [`kosiasuzu/agenticml-llama3.1-8b-lora-merged`](https://huggingface.co/kosiasuzu/agenticml-llama3.1-8b-lora-merged)
+
+## Uses
+
+### Direct Use
+
+- Continue multi-turn **ChatML** agent trajectories: system/user/tool messages, assistant generations with `<|eot_id|>` / tool payloads.
+- Intended for **research comparison** against AgenticML-format models on the same tasks—not a general-purpose chat product.
+
+### Downstream Use
+
+- Load with `transformers` + `apply_chat_template` (instruct tokenizer bundled with this checkpoint). Do not use the base `meta-llama/Llama-3.1-8B` tokenizer — it has no `chat_template`.
+- Run format-validity or task benchmarks with `agenticml eval-benchmarks --suite format_validity --format chatml`.
+
+### Out-of-Scope Use
+
+- Not validated for open-ended consumer chat, code execution without sandboxing, or high-stakes decisions (finance, health, legal).
+- Do not assume parity with official Meta Instruct checkpoints; this is a **project-specific** init + LoRA on synthetic/agent SFT data.
+- Tool calls in training data are **simulated**; real APIs need your own schemas and safety layers.
+
+## Bias, Risks, and Limitations
+
+- Trained largely on **synthetic** agent trajectories; domain mix is fixed in the generation prompt (coding, finance, travel, etc.) and may not match production traffic.
+- Same **biases and safety gaps** as Llama 3.1 base and the synthetic generator.
+- ChatML structural metrics (stop tokens, tool JSON shape) do not guarantee **correct** tool choice or factual answers.
+- This repo is a **single merged checkpoint** for inference and eval (no separate adapter load path in the AgenticML CLI).
+
+### Recommendations
+
+- Use with explicit tool schemas and runtime validation.
+- Compare against `kosiasuzu/agenticml-llama3.1-8b-lora-merged` on identical eval splits for fair format A/B tests.
+- Report **domain-stratified** metrics (dataset includes a `domain` field).
+
+## Same task, AgenticML format (side-by-side)
+
+Paired checkpoint: [`kosiasuzu/agenticml-llama3.1-8b-lora-merged`](https://huggingface.co/kosiasuzu/agenticml-llama3.1-8b-lora-merged). Same dataset; ChatML uses `messages`, AgenticML uses `frames`.
+
+**Task:** read `main.py`, return line count.
+
+| ChatML (this model) | AgenticML (paired) |
+|---------------------|-------------------|
+| system + tools block in one message | separate `<\|goal\|>` + `<\|obs\|>` frames |
+| user mission message | `<\|mission\|>` frame |
+| `<\|python_tag\|>{"name":"read_file",...}<\|eom_id\|>` | `<\|action\|>{"tool":"read_file",...}<\|end\|>` |
+| tool role with JSON content | `<\|result\|>` frame |
+| plain assistant answer text | `<\|action\|>{"tool":"answer","text":"..."}<\|end\|>` |
+
+Conversion: [`bridge.py`](../src/agenticml/bridge.py). Full pipeline: [`recipe.md`](../recipe.md).
+
+## How to Get Started with the Model
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "kosiasuzu/chatml-llama3.1-8b-lora-merged"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+model.eval()
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant with tools."},
+    {"role": "user", "content": "What is 17 * 23?"},
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    out = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        do_sample=False,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+text = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
+print(text)
+```
+
+**AgenticML CLI eval** (requires `pip install -e ".[eval]"` from the AgenticML repo):
+
+```bash
+agenticml eval-benchmarks --suite format_validity \
+  --format chatml \
+  --model kosiasuzu/chatml-llama3.1-8b-lora-merged \
+  --output-dir results/benchmarks/format_validity
+```
+
+## Training Details
+
+### Training Data
+
+- **Dataset:** `kosiasuzu/agenticml-agent-trajectory-dataset`
+- **Splits:** `train` / `eval` (stratified by `domain` at dataset build time)
+- **Field used:** `messages` (JSON string per row: ChatML message list from AgenticML `frames`)
+- **Rows without valid `messages` are dropped** during tokenization
+
+### Training Procedure
+
+**Command (representative):**
+
+```bash
+torchrun --standalone --nproc_per_node=2 -m agenticml.cli.commands.train_on_format --format chatml \
+  --model-id kosiasuzu/chatml-agent-llama-3.1-8b-init \
+  --dataset kosiasuzu/agenticml-agent-trajectory-dataset \
+  --hub-repo-id kosiasuzu/chatml-llama3.1-8b-lora-merged
+```
+
+**Tokenizer:** training loads the instruct tokenizer from the ChatML init checkpoint (`init-embeddings --format chatml`). Hub push stores the tokenizer on this merged repo alongside the weights.
+
+#### Preprocessing
+
+- Instruct tokenizer: `tokenizer.apply_chat_template(..., return_assistant_tokens_mask=True)` when supported; else assistant-span masking via token-id subsequence search.
+- Truncate to **`max_length=2048`** (CLI default for chatml train).
+
+#### Training Hyperparameters
+
+| Setting | Value |
+|--------|--------|
+| **Regime** | bf16, gradient checkpointing |
+| **LoRA rank** | r=32, alpha=64, dropout=0.05 |
+| **LoRA targets** | q/k/v/o_proj, gate/up/down_proj, **lm_head** |
+| **Epochs** | 2 |
+| **LR** | 2e-4, cosine, warmup 3% |
+| **Batch** | per-device 1 × grad accum 32 (effective batch scales with world size) |
+| **Max grad norm** | 1.0 |
+| **Weight decay** | 0.1 |
+| **Seed** | 42 |
+| **Multi-GPU** | FSDP full_shard + auto_wrap (when using `torchrun`) |
+| **Logging** | Weights & Biases (`project=agenticml-agent`, run name configurable) |
+
+#### Speeds, Sizes, Times
+
+Fill from your W&B / cluster logs (GPU type, world size, wall-clock, peak VRAM).
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+- **Held-out split:** `eval` on `kosiasuzu/agenticml-agent-trajectory-dataset`
+- **Same examples** as AgenticML eval (paired `frames` / `messages` columns)
+
+#### Factors
+
+- **Domain** (`coding`, `finance`, `travel`, …) — stratification in dataset builder
+
+#### Metrics
+
+- **Format validity** (AgenticML harness, `--format chatml`): parseable stop structure, tool-call JSON when present, non-empty assistant content
+- **Rates:** `parse_rate`, `structural_valid_rate` per domain (see eval JSON summary)
+
+### Results
+
+Upstream benchmark matrix (subset runs, seed 42). Full table: [`docs/benchmark_results.md`](../docs/benchmark_results.md). Regenerate: `agenticml eval-aggregate-results`.
+
+| Suite | n | Primary | Secondary | avg_tokens | avg_wall_sec |
+|-------|---|---------|-----------|------------|--------------|
+| bfcl | 5 | accuracy 60% | avg_retry 1.6 | 20,139 | 192s |
+| toolbench | 2 | pass 0% | avg_steps 12 | 40,350 | 848s |
+| format_validity | — | not run | — | — | — |
+| swe (lite) | — | not run | — | — | — |
+
+Paired AgenticML model on the same suites: [`agenticml-llama3.1-8b-lora-merged.md`](agenticml-llama3.1-8b-lora-merged.md).
+
+```bash
+agenticml eval-benchmarks --suite <bfcl|toolbench|swe|format_validity> --format chatml --model kosiasuzu/chatml-llama3.1-8b-lora-merged
+agenticml eval-run-all --dry-run
+agenticml eval-aggregate-results
+```
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+- **Architecture:** Llama 3.1 8B causal LM
+- **Objective:** causal LM fine-tuning with assistant-only labels (ChatML template)
+
+### Compute Infrastructure
+
+#### Hardware
+
+Multi-GPU training via `torchrun` (typical: 2× GPU with FSDP); document your setup when publishing.
+
+#### Software
+
+- Python ≥3.10, `transformers` ≥4.43, `peft`, `datasets`, `torch`
+- Training entrypoint: `agenticml train-on-format --format chatml` / `python -m agenticml.cli.commands.train_on_format`
+
+## Citation
+
+If you use this model in research, cite the AgenticML project repository and acknowledge Meta Llama 3.1.
+
+**APA:** [Author]. (Year). AgenticML: Agent-native serialization for language agents. GitHub repository. Baseline model: kosiasuzu/chatml-llama3.1-8b-lora-merged.
+
+**BibTeX:** Add when a formal report or arXiv entry is available.
+
+## Model Card Contact
+
+- Hugging Face: [kosiasuzu](https://huggingface.co/kosiasuzu)
+- Issues: AgenticML repository issue tracker

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 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 %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 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": 128009,
+  "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": "5.10.2",
+  "use_cache": false,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128009,
+    128001
+  ],
+  "pad_token_id": 128009,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.10.2"
+}

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+}

special_tokens_map.json

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+{
+  "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
+  }
+}

tokenizer_config.json

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+{
+  "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": [
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+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "TokenizersBackend"
+}