Instructions to use harsh762011/lora22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use harsh762011/lora22 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/phi-4-mini-reasoning-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "harsh762011/lora22")

Transformers

How to use harsh762011/lora22 with Transformers:

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

pipe = pipeline("text-generation", model="harsh762011/lora22")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("harsh762011/lora22", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use harsh762011/lora22 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harsh762011/lora22"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harsh762011/lora22",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/harsh762011/lora22

SGLang

How to use harsh762011/lora22 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 "harsh762011/lora22" \
    --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": "harsh762011/lora22",
		"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 "harsh762011/lora22" \
        --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": "harsh762011/lora22",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use harsh762011/lora22 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for harsh762011/lora22 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for harsh762011/lora22 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for harsh762011/lora22 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="harsh762011/lora22",
    max_seq_length=2048,
)

Docker Model Runner
How to use harsh762011/lora22 with Docker Model Runner:
```
docker model run hf.co/harsh762011/lora22
```

harsh762011 commited on Jan 7

Commit

d446367

verified ·

1 Parent(s): c15a039

Update LoRA weights

Browse files

Files changed (4) hide show

README.md +1 -1
adapter_config.json +9 -5
continue_lora/adapter_config.json +9 -5
continue_lora/adapter_model.safetensors +1 -1

README.md CHANGED Viewed

@@ -207,4 +207,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 [More Information Needed]
 ### Framework versions
-- PEFT 0.17.1

 [More Information Needed]
 ### Framework versions
+- PEFT 0.18.0

adapter_config.json CHANGED Viewed

@@ -1,5 +1,7 @@
 {
   "alpha_pattern": {},
   "auto_mapping": {
     "base_model_class": "Phi3ForCausalLM",
     "parent_library": "transformers.models.phi3.modeling_phi3",
@@ -8,6 +10,7 @@
   "base_model_name_or_path": "unsloth/phi-4-mini-reasoning-unsloth-bnb-4bit",
   "bias": "none",
   "corda_config": null,
   "eva_config": null,
   "exclude_modules": null,
   "fan_in_fan_out": false,
@@ -24,18 +27,19 @@
   "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
   "qalora_group_size": 16,
   "r": 16,
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "k_proj",
-    "gate_proj",
-    "up_proj",
-    "q_proj",
     "v_proj",
     "down_proj",
-    "o_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",

 {
+  "alora_invocation_tokens": null,
   "alpha_pattern": {},
+  "arrow_config": null,
   "auto_mapping": {
     "base_model_class": "Phi3ForCausalLM",
     "parent_library": "transformers.models.phi3.modeling_phi3",
   "base_model_name_or_path": "unsloth/phi-4-mini-reasoning-unsloth-bnb-4bit",
   "bias": "none",
   "corda_config": null,
+  "ensure_weight_tying": false,
   "eva_config": null,
   "exclude_modules": null,
   "fan_in_fan_out": false,
   "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
+  "peft_version": "0.18.0",
   "qalora_group_size": 16,
   "r": 16,
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
     "v_proj",
+    "up_proj",
     "down_proj",
+    "q_proj",
+    "o_proj",
+    "gate_proj",
+    "k_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",

continue_lora/adapter_config.json CHANGED Viewed

@@ -1,5 +1,7 @@
 {
   "alpha_pattern": {},
   "auto_mapping": {
     "base_model_class": "Phi3ForCausalLM",
     "parent_library": "transformers.models.phi3.modeling_phi3",
@@ -8,6 +10,7 @@
   "base_model_name_or_path": "unsloth/phi-4-mini-reasoning-unsloth-bnb-4bit",
   "bias": "none",
   "corda_config": null,
   "eva_config": null,
   "exclude_modules": null,
   "fan_in_fan_out": false,
@@ -24,18 +27,19 @@
   "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
   "qalora_group_size": 16,
   "r": 16,
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "k_proj",
-    "gate_proj",
-    "up_proj",
-    "q_proj",
     "v_proj",
     "down_proj",
-    "o_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",

 {
+  "alora_invocation_tokens": null,
   "alpha_pattern": {},
+  "arrow_config": null,
   "auto_mapping": {
     "base_model_class": "Phi3ForCausalLM",
     "parent_library": "transformers.models.phi3.modeling_phi3",
   "base_model_name_or_path": "unsloth/phi-4-mini-reasoning-unsloth-bnb-4bit",
   "bias": "none",
   "corda_config": null,
+  "ensure_weight_tying": false,
   "eva_config": null,
   "exclude_modules": null,
   "fan_in_fan_out": false,
   "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
+  "peft_version": "0.18.0",
   "qalora_group_size": 16,
   "r": 16,
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
     "v_proj",
+    "up_proj",
     "down_proj",
+    "q_proj",
+    "o_proj",
+    "gate_proj",
+    "k_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",

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