Instructions to use shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b with Transformers:

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

pipe = pipeline("text-generation", model="shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b")
model = AutoModelForCausalLM.from_pretrained("shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b")
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/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b"
# 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/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b

SGLang

How to use shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b 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/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b" \
    --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/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b",
		"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/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b" \
        --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/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b with Docker Model Runner:
```
docker model run hf.co/shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

See axolotl config

axolotl version: 0.8.0.dev0

# train w/ shisa-ai/shisa-v1-athenev2-reannotated-filtered

base_model: stabilityai/japanese-stablelm-base-gamma-7b
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

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

chat_template: llama3
datasets:
  - path: shisa-ai/shisa-v1-athenev2-reannotated-filtered
    # type: sharegpt deprecated
    type: chat_template
    field_messages: conversations
    message_field_role: from
    message_field_content: value
  - path: shisa-ai/shisa-v2-roleplaying
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: role
      content: content
    roles:
      system:
        - system
      assistant:
        - gpt
        - model
        - assistant
      user:
        - human
        - user
    roles_to_train: ["assistant"]
  - path: shisa-ai/translation-master-set
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: role
      content: content
    roles:
      system:
        - system
      assistant:
        - gpt
        - model
        - assistant
      user:
        - human
        - user
    roles_to_train: ["assistant"]
    
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

# marginal difference
neftune_noise_alpha: 5

use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b

gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 8e-6

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 0
save_total_limit: 1 # Only store a single checkpoint
debug:
deepspeed: zero3_bf16.json
weight_decay: 1e-4
fsdp:
fsdp_config:
special_tokens:
  pad_token: "</s>"

outputs/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b

This model is a fine-tuned version of stabilityai/japanese-stablelm-base-gamma-7b on the shisa-ai/shisa-v1-athenev2-reannotated-filtered, the shisa-ai/shisa-v2-roleplaying and the shisa-ai/translation-master-set datasets. It achieves the following results on the evaluation set:

Loss: 0.5072

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: 8e-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.8277	0.0024	1	0.7805
0.4465	0.5006	207	0.4703
0.2297	1.0	414	0.4434
0.2821	1.5006	621	0.4535
0.1233	2.0	828	0.4454
0.1128	2.5006	1035	0.5072

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 1

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for shisa-ai/ablation-56-rafathenev2.rp.tl.gamma-shisa-v2-gamma-7b

Base model

stabilityai/japanese-stablelm-base-gamma-7b

Finetuned

(3)

this model

Quantizations

1 model