Instructions to use neginashz/star-sft-intellect-instruct-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neginashz/star-sft-intellect-instruct-5 with Transformers:

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

pipe = pipeline("text-generation", model="neginashz/star-sft-intellect-instruct-5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neginashz/star-sft-intellect-instruct-5")
model = AutoModelForCausalLM.from_pretrained("neginashz/star-sft-intellect-instruct-5")
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

vLLM

How to use neginashz/star-sft-intellect-instruct-5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neginashz/star-sft-intellect-instruct-5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neginashz/star-sft-intellect-instruct-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neginashz/star-sft-intellect-instruct-5

SGLang

How to use neginashz/star-sft-intellect-instruct-5 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 "neginashz/star-sft-intellect-instruct-5" \
    --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": "neginashz/star-sft-intellect-instruct-5",
		"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 "neginashz/star-sft-intellect-instruct-5" \
        --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": "neginashz/star-sft-intellect-instruct-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use neginashz/star-sft-intellect-instruct-5 with Docker Model Runner:
```
docker model run hf.co/neginashz/star-sft-intellect-instruct-5
```

See axolotl config

axolotl version: 0.6.0

base_model: PrimeIntellect/INTELLECT-1-Instruct
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

gpu_memory_limit: 

deepspeed: deepspeed_configs/zero2.json

load_in_8bit: 
load_in_4bit:
strict: false

chat_template: llama3
datasets:
  - path: neginashz/rationale-llama-chat-dataset
    type: chat_template
    chat_template: llama3
    field_messages: messages
    message_field_role: role
    message_field_content: content
    roles:
      system:
        - system
      user:
        - user
      assistant:
        - assistant
    #roles_to_train: ["assistant"]  # default
    # Optional[str]. Which EOS tokens to train on in the conversation. Possible values are:
    # - all: train on all EOS tokens
    # - turn (default): train on the EOS token at the end of each trainable turn
    # - last: train on the last EOS token in the conversation
    #train_on_eos: turn

    
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./star-sft-intellect-5

sequence_len: 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true


wandb_project: star-sft-intellect-instruct-5
wandb_entity: 
wandb_watch:
wandb_name: 
wandb_log_model: 

gradient_checkpointing: true
#gradient_clipping: true
gradient_accumulation_steps: 1
#batch_size: 1
micro_batch_size: 1

num_epochs: 1

optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00002

train_on_inputs: false
group_by_length: false

bf16: true
fp16: false
tf32: false

logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps:
eval_steps: 
save_steps:

evals_per_epoch: 16
saves_per_epoch: 4
eval_max_new_tokens: 128

debug:

weight_decay:
fsdp:
fsdp_config:

hub_model_id: neginashz/star-sft-intellect-instruct-5
hub_strategy: 
early_stopping_patience:

resume_from_checkpoint:
auto_resume_from_checkpoints: true

#special_tokens:
#   pad_token: <|end_of_text|>

star-sft-intellect-instruct-5

This model is a fine-tuned version of PrimeIntellect/INTELLECT-1-Instruct on the neginashz/rationale-llama-chat-dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.3364

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 4
total_eval_batch_size: 4
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 6
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.4105	0.0664	15	0.4274
0.4759	0.1327	30	0.4348
0.4704	0.1991	45	0.4255
0.4612	0.2655	60	0.4167
0.4765	0.3319	75	0.4030
0.4022	0.3982	90	0.3932
0.4234	0.4646	105	0.3856
0.4008	0.5310	120	0.3736
0.4066	0.5973	135	0.3649
0.4007	0.6637	150	0.3568
0.4059	0.7301	165	0.3491
0.3622	0.7965	180	0.3429
0.3655	0.8628	195	0.3388
0.3655	0.9292	210	0.3368
0.3868	0.9956	225	0.3364

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.21.0

Downloads last month: 1

Safetensors

Model size

10B params

Tensor type

BF16

Model tree for neginashz/star-sft-intellect-instruct-5

Base model

PrimeIntellect/INTELLECT-1

Finetuned

PrimeIntellect/INTELLECT-1-Instruct

Finetuned

(5)

this model

neginashz
/

star-sft-intellect-instruct-5

star-sft-intellect-instruct-5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for neginashz/star-sft-intellect-instruct-5

Dataset used to train neginashz/star-sft-intellect-instruct-5