Samantha Qwen2 7B
Trained on 2x4090 using QLoRa and FSDP
Launch Using VLLM
python -m vllm.entrypoints.openai.api_server \
--model macadeliccc/Samantha-Qwen-2-7B \
--chat-template ./examples/template_chatml.jinja \
from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
chat_response = client.chat.completions.create(
model="macadeliccc/Samantha-Qwen-2-7B",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Tell me a joke."},
]
)
print("Chat response:", chat_response)
Prompt Template
<|im_start|>system
You are a friendly assistant.<|im_end|>
<|im_start|>user
What is the capital of France?<|im_end|>
<|im_start|>assistant
The capital of France is Paris.
Quants
See axolotl config
axolotl version: 0.4.0
base_model: Qwen/Qwen-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: macadeliccc/opus_samantha
type: sharegpt
field: conversations
conversation: chatml
- path: uncensored-ultrachat.json
type: sharegpt
field: conversations
conversation: chatml
- path: openhermes_200k.json
type: sharegpt
field: conversations
conversation: chatml
- path: opus_instruct.json
type: sharegpt
field: conversations
conversation: chatml
chat_template: chatml
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out
sequence_len: 2048
sample_packing: false
pad_to_sequence_len:
adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:
warmup_steps: 250
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
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