Instructions to use amphora/fc-proj1-test01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amphora/fc-proj1-test01 with Transformers:

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

pipe = pipeline("text-generation", model="amphora/fc-proj1-test01")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amphora/fc-proj1-test01")
model = AutoModelForCausalLM.from_pretrained("amphora/fc-proj1-test01", device_map="auto")
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 amphora/fc-proj1-test01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amphora/fc-proj1-test01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amphora/fc-proj1-test01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amphora/fc-proj1-test01

SGLang

How to use amphora/fc-proj1-test01 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 "amphora/fc-proj1-test01" \
    --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": "amphora/fc-proj1-test01",
		"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 "amphora/fc-proj1-test01" \
        --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": "amphora/fc-proj1-test01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amphora/fc-proj1-test01 with Docker Model Runner:
```
docker model run hf.co/amphora/fc-proj1-test01
```

See axolotl config

axolotl version: 0.10.0

# base_model: mistralai/Mistral-Nemo-Base-2407
base_model: kakaocorp/kanana-1.5-2.1b-instruct-2505

# Enable to use mistral-common tokenizer
# tokenizer_use_mistral_common: true

# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

load_in_8bit: false
load_in_4bit: false

# datasets:
#   - path: fozziethebeat/alpaca_messages_2k_test
#     type: chat_template

datasets:
  - path: train.jsonl
    type: chat_template

dataset_prepared_path: preprocess
val_set_size: 0.01
output_dir: ./outputs
dataloader_num_workers: 56

adapter: 
# adapter: lora
lora_model_dir:

# lora_r: 32
# lora_alpha: 16
# lora_dropout: 0.05
# lora_target_linear: true
# lora_target_modules:
#   - gate_proj
#   - down_proj
#   - up_proj
#   - q_proj
#   - v_proj
#   - k_proj
#   - o_proj

# lora_mlp_kernel: true
# lora_qkv_kernel: true
# lora_o_kernel: true

sequence_len: 8192
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

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

wandb_project: fastcampus
wandb_entity:
wandb_watch:
wandb_name: fc-proj1-test01
wandb_log_model:
hub_model_id: amphora/fc-proj1-test01

gradient_accumulation_steps: 4
micro_batch_size: 16
num_epochs: 3
optimizer: adamw_torch_fused
# optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 2e-5

bf16: auto
tf32: false

# torch_compile: auto
# torch_compile_backend: inductor

gradient_checkpointing:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true
# flash_attn_rms_norm: true
# flash_attn_cross_entropy: true
# flash_attn_fuse_qkv: true
flash_attn_fuse_mlp: true

warmup_ratio: 0.05
# warmup_steps: 10
weight_decay: 0.01
evals_per_epoch: 0
saves_per_epoch: 1

# deepspeed: deepspeed_configs/zero3_bf16.json
# fsdp:
#   # - shard_grad_ops
#   - full_shard
#   - auto_wrap
# fsdp_config:
#   fsdp_state_dict_type: FULL_STATE_DICT
#   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
#   fsdp_activation_checkpointing: true

fsdp:
  # - shard_grad_ops
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_backward_prefetch: BACKWARD_PRE
  fsdp_state_dict_type: SHARDED_STATE_DICT
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
  fsdp_activation_checkpointing: true

fc-proj1-test01

This model is a fine-tuned version of kakaocorp/kanana-1.5-2.1b-instruct-2505 on the train.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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 43
training_steps: 860

Training results

Framework versions

Transformers 4.52.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

Downloads last month: 6

Safetensors

Model size

2B params

Tensor type

F32

Model tree for amphora/fc-proj1-test01

Base model

kakaocorp/kanana-1.5-2.1b-instruct-2505

Finetuned

(77)

this model