Instructions to use pandeyankit84/autoscientist-toolcaller with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pandeyankit84/autoscientist-toolcaller with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pandeyankit84/autoscientist-toolcaller")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("pandeyankit84/autoscientist-toolcaller", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pandeyankit84/autoscientist-toolcaller with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pandeyankit84/autoscientist-toolcaller"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pandeyankit84/autoscientist-toolcaller",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pandeyankit84/autoscientist-toolcaller

SGLang

How to use pandeyankit84/autoscientist-toolcaller with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pandeyankit84/autoscientist-toolcaller" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pandeyankit84/autoscientist-toolcaller",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pandeyankit84/autoscientist-toolcaller" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pandeyankit84/autoscientist-toolcaller",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pandeyankit84/autoscientist-toolcaller with Docker Model Runner:
```
docker model run hf.co/pandeyankit84/autoscientist-toolcaller
```

autoscientist-toolcaller

Status: weights pending. This repository currently ships the dataset, the eval harness, and a deterministic behavior demo (a simulator, not the trained model). The trained weights come from the AutoScientist training run and will be added here, together with the official per-category held-out number. The headline below is Adaptive Data's dataset-quality grade — not a model accuracy.

A function-calling / tool-use model + dataset for the AutoScientist Challenge (category: All Other Domains). The dataset teaches a model the decision most tool-use datasets ignore: when not to call a tool.

Adaptive Data result (real). On a completed, uncapped run of the current-generation set (bea4a581…, 5,133/5,157 rows processed, 2026-07-05) the platform reports 7.0 → 8.1, +15.7%, grade C → B — with completion quality +31.5% (6.92 → 9.1) and the dataset's quality percentile rising 8.4 → 31.5. Independently corroborated by the older capped c4923b7f run (+15.7%) and a completed 250-row run (+10.0%, grade B, a99c0c96…). This is Adaptive Data's dataset-quality grade — the data-centric measurable improvement; the held-out model number comes from the AutoScientist training run. Intended base: Qwen/Qwen2.5-Coder-3B-Instruct.

What's different

The training set mixes standard tool-call examples with a large slice of hard negatives — cases where the correct behavior is refuse (no applicable tool), clarify (a required argument is missing), or disambiguate (two plausible tools) — plus multi-turn (BFCL miss_param / miss_func / long_context) and schema-drift (tools whose schema changed) slices. Baselines hallucinate tool calls in exactly these cases; this model is trained not to.

Every answer is a single JSON envelope: {"action": "call"|"refuse"|"clarify", "calls": [...], "message": "..."}.

Results

Adaptive Data quality (real, the headline). Adaptive Data is a data-centric platform: it improves the dataset and grades the improvement, so its quality grade is the challenge's measurable improvement.

Run	Rows	score before → after	Δ	grade
Fixed dataset (`c4923b7f…`)	2,440	7.0 → 8.1	+15.7%	C → B
Earlier curated (`a99c0c96…`, completed)	250	8.0 → 8.8	+10.0%	B

Model accuracy (base vs. fine-tuned). The full base-vs-fine-tuned table (overall / positive / refusal / clarify / hallucination-rate / novel-tools-holdout accuracy) requires training a model on the improved dataset, which needs a GPU. The harness is ready and one-command: bash scripts/run_all.sh with MODEL=<hf-id> runs baseline → multi-seed eval → paired significance (eval_stats) → gap decomposition (eval_decompose) → robustness-delta → reliability probe → HTML report. Numbers land in results/ and auto-fill via python -m autoscientist_toolcaller.fill_model_card.

Intended use

Agent / tool-calling pipelines that need reliable JSON tool calls and safe abstention. Feed the available tools (JSON Schema) + the user request; the model returns one JSON envelope.

Training

Platform: Adaption AutoScientist (Adaptive Data recipes: deduplication + reasoning traces; brand-controls blueprint enforcing call/refuse/clarify discipline).
Data: curated from Team-ACE/ToolACE (Apache-2.0) + original synthetic hard-negative / multi-turn / schema-drift slices. Deduplicated (MinHash + semantic), cross-split leakage removed. Seed 42.

Limitations

English-only in this version.
Optimized for tool-calling reliability; not a general chat model.
Argument scoring uses relaxed matching; strict-format consumers should post-validate the JSON.

Model tree for pandeyankit84/autoscientist-toolcaller

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-Coder-3B

Finetuned

Qwen/Qwen2.5-Coder-3B-Instruct

Finetuned

(123)

this model

pandeyankit84
/

autoscientist-toolcaller

autoscientist-toolcaller

What's different

Results

Intended use

Training

Limitations

Links

Model tree for pandeyankit84/autoscientist-toolcaller

Dataset used to train pandeyankit84/autoscientist-toolcaller

Space using pandeyankit84/autoscientist-toolcaller 1