Instructions to use shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft with Transformers:

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

pipe = pipeline("text-generation", model="shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft")
model = AutoModelForCausalLM.from_pretrained("shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft")
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 shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft

SGLang

How to use shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft 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 "shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft" \
    --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": "shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft",
		"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 "shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft" \
        --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": "shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft with Docker Model Runner:
```
docker model run hf.co/shisa-ai/173-shisa-v1-7b-v2.1-midtrain-sft
```

See axolotl config

axolotl version: 0.13.0.dev0

# Axolotl SFT configuration for Qwen3-8B on 8x MI300X
# 2X LR

base_model: /data/outputs/172-shisa-v1-7b-v2.1-midtrain

load_in_8bit: false
load_in_4bit: false
strict: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

chat_template: tokenizer_default
chat_template_kwargs:
  enable_thinking: false
datasets:
  - path: sft.shisa-v2.1.jsonl
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: role
      content: content
    roles:
      system:
        - system
      assistant:
        - assistant
        - gpt
        - model
      user:
        - user
        - human
    roles_to_train: ["assistant"]

shuffle_merged_datasets: false
dataset_prepared_path: data/173
val_set_size: 0
output_dir: /data/outputs/173-shisa-v1-7b-v2.1-midtrain-sft

sequence_len: 8192
sample_packing: true
flash_attention: true
pad_to_sequence_len: true

neftune_noise_alpha: 5

use_wandb: true
wandb_entity: augmxnt
wandb_project: shisa-v2.1
wandb_name: 173-shisa-v1-7b-v2.1-midtrain-sft

# GBS 128 = 8 GPU x 16 MBS x 1 GAS
gradient_accumulation_steps: 1
micro_batch_size: 16
num_epochs: 3
optimizer: adamw_torch_8bit
lr_scheduler: cosine
learning_rate: 1e-05

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
logging_steps: 1

warmup_ratio: 0.03
saves_per_epoch: 1
save_total_limit: 3

deepspeed: zero3_bf16.json
weight_decay: 1e-4
fsdp:
fsdp_config:
special_tokens:

data/outputs/173-shisa-v1-7b-v2.1-midtrain-sft

This model was trained from scratch on the sft.shisa-v2.1.jsonl dataset.

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: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 128
total_eval_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH_8BIT 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: 32
training_steps: 1080

Training results

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+rocm6.4
Datasets 4.3.0
Tokenizers 0.22.1

Downloads last month: 1

Safetensors

Model size

266k params

Tensor type

BF16