π₯· Safurai-Csharp-34B
π Article
π Paper
![](https://i.imgur.com/REPqbYM.png)
This is a codellama/CodeLlama-34b-hf
model fine-tuned using QLoRA (4-bit precision) on 13B tokens of csharp evolved Q&A
We obtained state-of-the-art performance on the MultiPL-E code LLM benchmark for csharp, reaching 56% at pass@1 with n=5.
π» Quantization
These are GGUF quantized versions of Safurai-Csharp-34B, it has been made by using the amazing llama.cpp
library.
π§ Training
It was trained on 2 x NVIDIA A100 PCIe 80GB in 7h 40m with the following configuration file:
base_model: codellama/CodeLlama-34b-hf
base_model_config: codellama/CodeLlama-34b-hf
model_type: LlamaForCausalLM
tokenizer_type: CodeLlamaTokenizer
is_llama_derived_model: true
hub_model_id: "Safurai/Evol-csharp-v1"
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: Safurai/EvolInstruct-csharp-16k-13B-Alpaca
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./qlora-out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: codellama-csharp
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0003
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 40
eval_steps: 40
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
π Training loss curve:
![](https://i.imgur.com/rp1htuf.png)
π Dataset composition:
![](https://i.imgur.com/kTNXgGX.png)
π» Usage for GGUF
# disclaimer: you have to use the llama.gguf library to run this code
import os
model_list = [file for file in os.listdir(MODEL_NAME) if GGML_VERSION in file]
prompt = input("Enter your prompt: ")
chosen_method = input("Please specify the quantization method to run the model (options: " + ", ".join(model_list) + "): ")
# Verify the chosen method is in the list
if chosen_method not in model_list:
print("Invalid method chosen!")
else:
qtype = f"{MODEL_NAME}/{MODEL_NAME.lower()}.{GGML_VERSION}.{method}.bin"
!./llama.cpp/main -m {qtype} -n 128 --color -ngl 35 -p "{prompt}"
- Downloads last month
- 9
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.