Instructions to use Azazelle/L3-Tyche-8B-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azazelle/L3-Tyche-8B-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="Azazelle/L3-Tyche-8B-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azazelle/L3-Tyche-8B-v1.0")
model = AutoModelForCausalLM.from_pretrained("Azazelle/L3-Tyche-8B-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Azazelle/L3-Tyche-8B-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Azazelle/L3-Tyche-8B-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azazelle/L3-Tyche-8B-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Azazelle/L3-Tyche-8B-v1.0

SGLang

How to use Azazelle/L3-Tyche-8B-v1.0 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 "Azazelle/L3-Tyche-8B-v1.0" \
    --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": "Azazelle/L3-Tyche-8B-v1.0",
		"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 "Azazelle/L3-Tyche-8B-v1.0" \
        --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": "Azazelle/L3-Tyche-8B-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Azazelle/L3-Tyche-8B-v1.0 with Docker Model Runner:
```
docker model run hf.co/Azazelle/L3-Tyche-8B-v1.0
```

Azazelle commited on Jul 10, 2024

Commit

780ac7f

verified ·

1 Parent(s): f7cc9fc

Update README.md

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README.md +82 -19

README.md CHANGED Viewed

@@ -4,40 +4,103 @@ library_name: transformers
 tags:
 - mergekit
 - merge
 ---
-# FuYRtqI
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the SLERP merge method.
-### Models Merged
-The following models were included in the merge:
-* output/pre
-* output/donor
 ### Configuration
 The following YAML configuration was used to produce this model:
 ```yaml
-base_model: output/donor
 dtype: float32
 merge_method: slerp
 parameters:
   t:
-  - filter: mlp
-    value: 0.0
-  - value: 1.0
-slices:
-- sources:
-  - layer_range: [0, 32]
-    model: output/pre
-  - layer_range: [0, 32]
-    model: output/donor
 ```

 tags:
 - mergekit
 - merge
+- llama
+- conversational
+license: llama3
 ---
+# L3-Tyche-8B-v1.0
+![Tyche](https://huggingface.co/Azazelle/L3-Tyche-8B-v1.0/resolve/main/TPr9FmCtT0OxPuz2MXxjgw.webp)
+## About:
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+**Recommended Samplers:**
+```
+Temperature - 1.3
+TFS - 0.96
+Smoothing Factor - 0.3
+Smoothing Curve - 1.1
+Repetition Penalty - 1.08
+```
+### Merge Method
+This model was merged a series of model stock and lora merges, followed by ExPO and an attention swap. It uses a mix of smart and roleplay centered models to improve performance.
 ### Configuration
 The following YAML configuration was used to produce this model:
 ```yaml
+---
+models:
+  - model: Nitral-AI/Hathor_Tahsin-L3-8B-v0.85
+  - model: Nitral-AI/Hathor_Respawn-L3-8B-v0.8
+  - model: ChaoticNeutrals/Hathor_RP-v.01-L3-8B
+  - model: Sao10K/L3-8B-Stheno-v3.2
+  - model: yodayo-ai/nephra_v1.0
+  - model: HiroseKoichi/L3-8B-Lunar-Stheno
+  - model: Jellywibble/lora_120k_pref_data_ep2
+  - model: Jellywibble/qlora_120k_pref_data_ep1
+  - model: Jellywibble/meseca-20062024-c1
+  - model: Hastagaras/Jamet-8B-L3-MK.V-Blackroot
+  - model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B
+  - model: ResplendentAI/Nymph_8B+Azazelle/RP_Format_QuoteAsterisk_Llama3
+  - model: R136a1/Bungo-L3-8B
+  - model: maldv/badger-mu-llama-3-8b
+  - model: TheDrummer/Llama-3SOME-8B-v2
+  - model: Magpie-Align/Llama-3-8B-Magpie-Align-v0.1+Azazelle/Llama3_RP_ORPO_LoRA
+  - model: grimjim/Llama-3-Instruct-8B-SPPO-Iter3-SimPO-merge+Azazelle/Llama-3-8B-Abomination-LORA
+  - model: NousResearch/Hermes-2-Pro-Llama-3-8B+mpasila/Llama-3-Instruct-LiPPA-LoRA-8B
+  - model: MaziyarPanahi/Llama-3-8B-Instruct-v0.8+Azazelle/Llama-3-Sunfall-8b-lora
+  - model: openchat/openchat-3.6-8b-20240522+Azazelle/BlueMoon_Llama3
+  - model: collaiborateorg/Collaiborator-MEDLLM-Llama-3-8B-v2+Azazelle/llama3-8b-hikikomori-v0.4
+  - model: grimjim/Llama-3-Instruct-8B-SPPO-Iter3-SimPO-merge+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
+merge_method: model_stock
+base_model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
+dtype: float32
+vocab_type: bpe
+name: hq_rp
+---
+# ExPO
+models:
+  - model: hq_rp
+    parameters:
+      weight: 1.3
+merge_method: task_arithmetic
+base_model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
+parameters:
+  normalize: false
 dtype: float32
+vocab_type: bpe
+name: pre
+---
+# Attention Donor
+models:
+  - model: Nitral-AI/Hathor_Tahsin-L3-8B-v0.85
+  - model: Sao10K/L3-8B-Stheno-v3.2
+merge_method: model_stock
+base_model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
+dtype: float32
+vocab_type: bpe
+name: donor
+---
+# Attention swap?
+models:
+  - model: pre
 merge_method: slerp
+base_model: donor
 parameters:
   t:
+    - filter: mlp
+      value: 0
+    - value: 1
+dtype: float32
+vocab_type: bpe
 ```