Instructions to use Likithp/v10_fixed_s1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Likithp/v10_fixed_s1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Likithp/v10_fixed_s1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Likithp/v10_fixed_s1") model = AutoModelForMultimodalLM.from_pretrained("Likithp/v10_fixed_s1") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Likithp/v10_fixed_s1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Likithp/v10_fixed_s1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Likithp/v10_fixed_s1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Likithp/v10_fixed_s1
- SGLang
How to use Likithp/v10_fixed_s1 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 "Likithp/v10_fixed_s1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Likithp/v10_fixed_s1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Likithp/v10_fixed_s1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Likithp/v10_fixed_s1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Likithp/v10_fixed_s1 with Docker Model Runner:
docker model run hf.co/Likithp/v10_fixed_s1
| { | |
| "mode": "fixed", | |
| "seed": 1, | |
| "hf_repo": "Likithp/v10_fixed_s1", | |
| "base_model": "Qwen/Qwen2.5-0.5B", | |
| "dataset": "data/cs7_fixed_v3", | |
| "dataset_version": "cs7_v3", | |
| "trained_at": "2026-06-06T07:48:24.312420+00:00", | |
| "optimizer": "AdamW", | |
| "lr": 3e-05, | |
| "weight_decay": 0.0, | |
| "batch_size": 8, | |
| "grad_accum": 8, | |
| "effective_batch": 64, | |
| "epochs": 5, | |
| "warmup_steps": 200, | |
| "grad_clip": 0.5, | |
| "lr_schedule": "cosine", | |
| "checkpoint_criterion": "val_em", | |
| "max_seq_len": 256, | |
| "dtype": "torch.bfloat16", | |
| "eval_method": "teacher_forcing_argmax", | |
| "alias_groups": { | |
| "T1": [ | |
| "act", | |
| "cst", | |
| "emp", | |
| "inv", | |
| "ord", | |
| "spl", | |
| "txn" | |
| ], | |
| "T2": [ | |
| "brc", | |
| "ctg", | |
| "dpt", | |
| "empl", | |
| "ordr", | |
| "prd", | |
| "prj", | |
| "rgn", | |
| "shp", | |
| "tsk", | |
| "whs" | |
| ] | |
| }, | |
| "log_every_steps": 50, | |
| "eval_n": 500, | |
| "dry_run": false, | |
| "best_epoch": 5, | |
| "best_train_loss": null, | |
| "val_exact_match": 1.0, | |
| "val_outer_alias_acc": 1.0, | |
| "val_inner_alias_acc": 1.0, | |
| "val_t1_inner_alias_acc": 1.0, | |
| "val_t2_inner_alias_acc": 1.0, | |
| "val_t1_t2_gap_pp": 0.0, | |
| "val_inner_by_alias": { | |
| "act": { | |
| "correct": 64, | |
| "total": 64, | |
| "pct": 100.0, | |
| "token_group": "T1" | |
| }, | |
| "brc": { | |
| "correct": 50, | |
| "total": 50, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| }, | |
| "cst": { | |
| "correct": 45, | |
| "total": 45, | |
| "pct": 100.0, | |
| "token_group": "T1" | |
| }, | |
| "ctg": { | |
| "correct": 61, | |
| "total": 61, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| }, | |
| "dpt": { | |
| "correct": 56, | |
| "total": 56, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| }, | |
| "emp": { | |
| "correct": 51, | |
| "total": 51, | |
| "pct": 100.0, | |
| "token_group": "T1" | |
| }, | |
| "empl": { | |
| "correct": 59, | |
| "total": 59, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| }, | |
| "inv": { | |
| "correct": 45, | |
| "total": 45, | |
| "pct": 100.0, | |
| "token_group": "T1" | |
| }, | |
| "ord": { | |
| "correct": 67, | |
| "total": 67, | |
| "pct": 100.0, | |
| "token_group": "T1" | |
| }, | |
| "ordr": { | |
| "correct": 59, | |
| "total": 59, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| }, | |
| "prd": { | |
| "correct": 57, | |
| "total": 57, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| }, | |
| "prj": { | |
| "correct": 52, | |
| "total": 52, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| }, | |
| "rgn": { | |
| "correct": 70, | |
| "total": 70, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| }, | |
| "shp": { | |
| "correct": 60, | |
| "total": 60, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| }, | |
| "spl": { | |
| "correct": 59, | |
| "total": 59, | |
| "pct": 100.0, | |
| "token_group": "T1" | |
| }, | |
| "tsk": { | |
| "correct": 44, | |
| "total": 44, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| }, | |
| "txn": { | |
| "correct": 51, | |
| "total": 51, | |
| "pct": 100.0, | |
| "token_group": "T1" | |
| }, | |
| "whs": { | |
| "correct": 50, | |
| "total": 50, | |
| "pct": 100.0, | |
| "token_group": "T2" | |
| } | |
| }, | |
| "train_log": [ | |
| { | |
| "epoch": 1, | |
| "loss": 0.026036, | |
| "val_em": 1.0 | |
| }, | |
| { | |
| "epoch": 2, | |
| "loss": 1e-05, | |
| "val_em": 1.0 | |
| }, | |
| { | |
| "epoch": 3, | |
| "loss": 1e-05, | |
| "val_em": 1.0 | |
| }, | |
| { | |
| "epoch": 4, | |
| "loss": 1e-05, | |
| "val_em": 1.0 | |
| }, | |
| { | |
| "epoch": 5, | |
| "loss": 1e-05, | |
| "val_em": 1.0 | |
| } | |
| ] | |
| } |