Instructions to use sravanthib/testing-without-deepspeed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sravanthib/testing-without-deepspeed with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B") model = PeftModel.from_pretrained(base_model, "sravanthib/testing-without-deepspeed") - Transformers
How to use sravanthib/testing-without-deepspeed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sravanthib/testing-without-deepspeed") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sravanthib/testing-without-deepspeed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sravanthib/testing-without-deepspeed with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sravanthib/testing-without-deepspeed" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sravanthib/testing-without-deepspeed", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sravanthib/testing-without-deepspeed
- SGLang
How to use sravanthib/testing-without-deepspeed 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 "sravanthib/testing-without-deepspeed" \ --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": "sravanthib/testing-without-deepspeed", "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 "sravanthib/testing-without-deepspeed" \ --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": "sravanthib/testing-without-deepspeed", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use sravanthib/testing-without-deepspeed with Docker Model Runner:
docker model run hf.co/sravanthib/testing-without-deepspeed
Training in progress, step 10
Browse files- adapter_config.json +4 -4
- adapter_model.safetensors +1 -1
- metrics.json +1 -0
adapter_config.json
CHANGED
|
@@ -26,12 +26,12 @@
|
|
| 26 |
"revision": null,
|
| 27 |
"target_modules": [
|
| 28 |
"v_proj",
|
| 29 |
-
"
|
| 30 |
-
"gate_proj",
|
| 31 |
-
"up_proj",
|
| 32 |
"o_proj",
|
|
|
|
| 33 |
"down_proj",
|
| 34 |
-
"
|
|
|
|
| 35 |
],
|
| 36 |
"task_type": "CAUSAL_LM",
|
| 37 |
"trainable_token_indices": null,
|
|
|
|
| 26 |
"revision": null,
|
| 27 |
"target_modules": [
|
| 28 |
"v_proj",
|
| 29 |
+
"q_proj",
|
|
|
|
|
|
|
| 30 |
"o_proj",
|
| 31 |
+
"up_proj",
|
| 32 |
"down_proj",
|
| 33 |
+
"k_proj",
|
| 34 |
+
"gate_proj"
|
| 35 |
],
|
| 36 |
"task_type": "CAUSAL_LM",
|
| 37 |
"trainable_token_indices": null,
|
adapter_model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 48679352
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fe162099dcf87beaaceee4a653cc02983e241634fb98780856f4dfc7477ec413
|
| 3 |
size 48679352
|
metrics.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"run_name": "./output-DeepSeek-R1-Distill-Qwen-7B-squad-nemo-replicaa", "train_runtime": 143.4567, "train_samples_per_second": 11.153, "train_steps_per_second": 0.07, "total_flos": 5.5657843654656e+16, "train_loss": 1.3610095977783203, "epoch": 0.0182648401826484}
|