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Model: kosiasuzu/chatml-llama3.1-8b-lora-merged Source: Original Platform
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README.md
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---
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library_name: transformers
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license: llama3.1
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base_model: kosiasuzu/chatml-agent-llama-3.1-8b-init
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tags:
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- llama-3.1
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- chatml
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- lora
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- agent
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- tool-use
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- agenticml-baseline
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pipeline_tag: text-generation
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---
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# kosiasuzu/chatml-llama3.1-8b-lora-merged
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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.
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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).
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## Model Details
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### Model Description
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- **Model ID:** `kosiasuzu/chatml-llama3.1-8b-lora-merged`
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- **Developed by:** AgenticML project / kosiasuzu
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- **Model type:** Causal language model (Llama 3.1 8B), **PEFT LoRA merged** into dense weights
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- **Language(s):** English (primary)
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- **License:** [Llama 3.1 Community License](https://llama.meta.com/llama3_1/license/) (inherits from Meta Llama 3.1 base; confirm before commercial use)
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- **Finetuned from:** [`kosiasuzu/chatml-agent-llama-3.1-8b-init`](https://huggingface.co/kosiasuzu/chatml-agent-llama-3.1-8b-init)
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**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`).
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**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.
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### Model Sources
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- **AgenticML repository:** https://github.com/kosiasuzu/agenticml
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- **Dataset:** [`kosiasuzu/agenticml-agent-trajectory-dataset`](https://huggingface.co/datasets/kosiasuzu/agenticml-agent-trajectory-dataset)
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- **Paired AgenticML merged model:** [`kosiasuzu/agenticml-llama3.1-8b-lora-merged`](https://huggingface.co/kosiasuzu/agenticml-llama3.1-8b-lora-merged)
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## Uses
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### Direct Use
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- Continue multi-turn **ChatML** agent trajectories: system/user/tool messages, assistant generations with `<|eot_id|>` / tool payloads.
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- Intended for **research comparison** against AgenticML-format models on the same tasks—not a general-purpose chat product.
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### Downstream Use
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- 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`.
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- Run format-validity or task benchmarks with `agenticml eval-benchmarks --suite format_validity --format chatml`.
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### Out-of-Scope Use
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- Not validated for open-ended consumer chat, code execution without sandboxing, or high-stakes decisions (finance, health, legal).
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- Do not assume parity with official Meta Instruct checkpoints; this is a **project-specific** init + LoRA on synthetic/agent SFT data.
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- Tool calls in training data are **simulated**; real APIs need your own schemas and safety layers.
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## Bias, Risks, and Limitations
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- Trained largely on **synthetic** agent trajectories; domain mix is fixed in the generation prompt (coding, finance, travel, etc.) and may not match production traffic.
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- Same **biases and safety gaps** as Llama 3.1 base and the synthetic generator.
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- ChatML structural metrics (stop tokens, tool JSON shape) do not guarantee **correct** tool choice or factual answers.
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- This repo is a **single merged checkpoint** for inference and eval (no separate adapter load path in the AgenticML CLI).
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### Recommendations
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- Use with explicit tool schemas and runtime validation.
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- Compare against `kosiasuzu/agenticml-llama3.1-8b-lora-merged` on identical eval splits for fair format A/B tests.
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- Report **domain-stratified** metrics (dataset includes a `domain` field).
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## Same task, AgenticML format (side-by-side)
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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`.
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**Task:** read `main.py`, return line count.
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| ChatML (this model) | AgenticML (paired) |
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|---------------------|-------------------|
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| system + tools block in one message | separate `<\|goal\|>` + `<\|obs\|>` frames |
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| user mission message | `<\|mission\|>` frame |
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| `<\|python_tag\|>{"name":"read_file",...}<\|eom_id\|>` | `<\|action\|>{"tool":"read_file",...}<\|end\|>` |
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| tool role with JSON content | `<\|result\|>` frame |
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| plain assistant answer text | `<\|action\|>{"tool":"answer","text":"..."}<\|end\|>` |
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Conversion: [`bridge.py`](../src/agenticml/bridge.py). Full pipeline: [`recipe.md`](../recipe.md).
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## How to Get Started with the Model
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "kosiasuzu/chatml-llama3.1-8b-lora-merged"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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model.eval()
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messages = [
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{"role": "system", "content": "You are a helpful assistant with tools."},
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{"role": "user", "content": "What is 17 * 23?"},
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(
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**inputs,
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max_new_tokens=512,
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do_sample=False,
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pad_token_id=tokenizer.eos_token_id,
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)
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text = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
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print(text)
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```
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**AgenticML CLI eval** (requires `pip install -e ".[eval]"` from the AgenticML repo):
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```bash
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agenticml eval-benchmarks --suite format_validity \
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--format chatml \
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--model kosiasuzu/chatml-llama3.1-8b-lora-merged \
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--output-dir results/benchmarks/format_validity
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```
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## Training Details
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### Training Data
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- **Dataset:** `kosiasuzu/agenticml-agent-trajectory-dataset`
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- **Splits:** `train` / `eval` (stratified by `domain` at dataset build time)
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- **Field used:** `messages` (JSON string per row: ChatML message list from AgenticML `frames`)
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- **Rows without valid `messages` are dropped** during tokenization
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### Training Procedure
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**Command (representative):**
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```bash
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torchrun --standalone --nproc_per_node=2 -m agenticml.cli.commands.train_on_format --format chatml \
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--model-id kosiasuzu/chatml-agent-llama-3.1-8b-init \
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--dataset kosiasuzu/agenticml-agent-trajectory-dataset \
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--hub-repo-id kosiasuzu/chatml-llama3.1-8b-lora-merged
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```
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**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.
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#### Preprocessing
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- Instruct tokenizer: `tokenizer.apply_chat_template(..., return_assistant_tokens_mask=True)` when supported; else assistant-span masking via token-id subsequence search.
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- Truncate to **`max_length=2048`** (CLI default for chatml train).
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#### Training Hyperparameters
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| Setting | Value |
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|--------|--------|
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| **Regime** | bf16, gradient checkpointing |
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| **LoRA rank** | r=32, alpha=64, dropout=0.05 |
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| **LoRA targets** | q/k/v/o_proj, gate/up/down_proj, **lm_head** |
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| **Epochs** | 2 |
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| **LR** | 2e-4, cosine, warmup 3% |
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| **Batch** | per-device 1 × grad accum 32 (effective batch scales with world size) |
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| **Max grad norm** | 1.0 |
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| **Weight decay** | 0.1 |
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| **Seed** | 42 |
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| **Multi-GPU** | FSDP full_shard + auto_wrap (when using `torchrun`) |
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| **Logging** | Weights & Biases (`project=agenticml-agent`, run name configurable) |
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#### Speeds, Sizes, Times
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Fill from your W&B / cluster logs (GPU type, world size, wall-clock, peak VRAM).
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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- **Held-out split:** `eval` on `kosiasuzu/agenticml-agent-trajectory-dataset`
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- **Same examples** as AgenticML eval (paired `frames` / `messages` columns)
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#### Factors
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- **Domain** (`coding`, `finance`, `travel`, …) — stratification in dataset builder
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#### Metrics
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- **Format validity** (AgenticML harness, `--format chatml`): parseable stop structure, tool-call JSON when present, non-empty assistant content
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- **Rates:** `parse_rate`, `structural_valid_rate` per domain (see eval JSON summary)
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### Results
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Upstream benchmark matrix (subset runs, seed 42). Full table: [`docs/benchmark_results.md`](../docs/benchmark_results.md). Regenerate: `agenticml eval-aggregate-results`.
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| Suite | n | Primary | Secondary | avg_tokens | avg_wall_sec |
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|-------|---|---------|-----------|------------|--------------|
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| bfcl | 5 | accuracy 60% | avg_retry 1.6 | 20,139 | 192s |
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| toolbench | 2 | pass 0% | avg_steps 12 | 40,350 | 848s |
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| format_validity | — | not run | — | — | — |
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| swe (lite) | — | not run | — | — | — |
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Paired AgenticML model on the same suites: [`agenticml-llama3.1-8b-lora-merged.md`](agenticml-llama3.1-8b-lora-merged.md).
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```bash
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agenticml eval-benchmarks --suite <bfcl|toolbench|swe|format_validity> --format chatml --model kosiasuzu/chatml-llama3.1-8b-lora-merged
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agenticml eval-run-all --dry-run
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agenticml eval-aggregate-results
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```
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## Technical Specifications
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### Model Architecture and Objective
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- **Architecture:** Llama 3.1 8B causal LM
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- **Objective:** causal LM fine-tuning with assistant-only labels (ChatML template)
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### Compute Infrastructure
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#### Hardware
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Multi-GPU training via `torchrun` (typical: 2× GPU with FSDP); document your setup when publishing.
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#### Software
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- Python ≥3.10, `transformers` ≥4.43, `peft`, `datasets`, `torch`
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- Training entrypoint: `agenticml train-on-format --format chatml` / `python -m agenticml.cli.commands.train_on_format`
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## Citation
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If you use this model in research, cite the AgenticML project repository and acknowledge Meta Llama 3.1.
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**APA:** [Author]. (Year). AgenticML: Agent-native serialization for language agents. GitHub repository. Baseline model: kosiasuzu/chatml-llama3.1-8b-lora-merged.
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**BibTeX:** Add when a formal report or arXiv entry is available.
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## Model Card Contact
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- Hugging Face: [kosiasuzu](https://huggingface.co/kosiasuzu)
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- Issues: AgenticML repository issue tracker
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109
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- "<|python_tag|>" + tool_call.name + ".call(" }}
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{%- for arg_name, arg_val in tool_call.arguments | items %}
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{{- arg_name + '="' + arg_val + '"' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- ")" }}
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{%- else %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- "}" }}
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{#- This means we're in ipython mode #}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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13
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||||
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|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
"content": "<|eot_id|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
15
tokenizer_config.json
Normal file
15
tokenizer_config.json
Normal file
@@ -0,0 +1,15 @@
|
||||
{
|
||||
"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,
|
||||
"pad_token": "<|eot_id|>",
|
||||
"tokenizer_class": "TokenizersBackend"
|
||||
}
|
||||
Reference in New Issue
Block a user