Text Generation
Transformers
Safetensors
code
ivme_coder
language-model
transformer
rope
swiglu
muon
from-scratch
tiny
small
decoder-only
python
custom_code
Instructions to use IvmeLabs/Ivme-Coder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IvmeLabs/Ivme-Coder-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IvmeLabs/Ivme-Coder-v1", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("IvmeLabs/Ivme-Coder-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IvmeLabs/Ivme-Coder-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IvmeLabs/Ivme-Coder-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IvmeLabs/Ivme-Coder-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IvmeLabs/Ivme-Coder-v1
- SGLang
How to use IvmeLabs/Ivme-Coder-v1 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 "IvmeLabs/Ivme-Coder-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IvmeLabs/Ivme-Coder-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "IvmeLabs/Ivme-Coder-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IvmeLabs/Ivme-Coder-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IvmeLabs/Ivme-Coder-v1 with Docker Model Runner:
docker model run hf.co/IvmeLabs/Ivme-Coder-v1
Upload Ivme-Coder-v1 (Otter 1): safetensors + custom modeling code
Browse files- README.md +1 -1
- config.json +1 -1
- model.safetensors +2 -2
README.md
CHANGED
|
@@ -64,7 +64,7 @@ import torch
|
|
| 64 |
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 65 |
|
| 66 |
model = AutoModelForCausalLM.from_pretrained(
|
| 67 |
-
"IvmeLabs/Ivme-Coder-v1", trust_remote_code=True, dtype=torch.
|
| 68 |
)
|
| 69 |
tokenizer = AutoTokenizer.from_pretrained("IvmeLabs/Ivme-Coder-v1", trust_remote_code=True)
|
| 70 |
model.eval()
|
|
|
|
| 64 |
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 65 |
|
| 66 |
model = AutoModelForCausalLM.from_pretrained(
|
| 67 |
+
"IvmeLabs/Ivme-Coder-v1", trust_remote_code=True, dtype=torch.float32,
|
| 68 |
)
|
| 69 |
tokenizer = AutoTokenizer.from_pretrained("IvmeLabs/Ivme-Coder-v1", trust_remote_code=True)
|
| 70 |
model.eval()
|
config.json
CHANGED
|
@@ -15,6 +15,6 @@
|
|
| 15 |
"AutoConfig": "modeling_ivme_coder.IvmeCoderConfig",
|
| 16 |
"AutoModelForCausalLM": "modeling_ivme_coder.IvmeCoderV1ForCausalLM"
|
| 17 |
},
|
| 18 |
-
"torch_dtype": "
|
| 19 |
"transformers_version": "4.x"
|
| 20 |
}
|
|
|
|
| 15 |
"AutoConfig": "modeling_ivme_coder.IvmeCoderConfig",
|
| 16 |
"AutoModelForCausalLM": "modeling_ivme_coder.IvmeCoderV1ForCausalLM"
|
| 17 |
},
|
| 18 |
+
"torch_dtype": "float32",
|
| 19 |
"transformers_version": "4.x"
|
| 20 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cb47d80da9d26d9447718597e01c1619c35d850d1ad60b6d78d78e69db0e3b97
|
| 3 |
+
size 184018856
|