library_name, license, base_model, tags, pipeline_tag

library_name	license	base_model	tags	pipeline_tag
transformers	llama3.1	kosiasuzu/chatml-agent-llama-3.1-8b-init	llama-3.1 chatml lora agent tool-use agenticml-baseline	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 (inherits from Meta Llama 3.1 base; confirm before commercial use)
• Finetuned from: 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
• Paired AgenticML merged model: 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. 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. Full pipeline: recipe.md.

How to Get Started with the Model

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):

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):

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. 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 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
• Issues: AgenticML repository issue tracker