meow

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README.md

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 ---
 license: cc-by-nc-2.0
+pipeline_tag: text-generation
+inference: false
+library_name: transformers
+base_model: cognitivecomputations/laserxtral
+tags:
+    - text-generation
 ---
+
+This is just, SOTA 2 and 3-bit quants for laserxtral. Not much more to it. Meow.
+
+The importance matrix, [which is generated from `20k_random_data.txt`](https://github.com/ggerganov/llama.cpp/discussions/5006#discussioncomment-8163190), is included in this repo, as `imatrix_laserxtral.dat`.
+
+## System Prompt
+
+Alpaca format
+```
+### Instruction:
+...
+### Input:
+...
+### Response:
+```
+
+If you use LM Studio, this repo has a `model_config.json` you can import which has that pre-configured.

model_config.json

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+{
+  "name": "Laserxtral (Alpaca)",
+  "load_params": {
+    "n_ctx": 4096,
+    "n_batch": 512,
+    "rope_freq_base": 0,
+    "rope_freq_scale": 0,
+    "n_gpu_layers": 0,
+    "use_mlock": true,
+    "main_gpu": 0,
+    "tensor_split": [
+      0
+    ],
+    "seed": -1,
+    "f16_kv": true,
+    "use_mmap": true,
+    "no_kv_offload": false,
+    "num_experts_used": 0
+  },
+  "inference_params": {
+    "n_threads": 4,
+    "n_predict": -1,
+    "top_k": 40,
+    "min_p": 0.05,
+    "top_p": 0.95,
+    "temp": 0.8,
+    "repeat_penalty": 1.1,
+    "input_prefix": "### Instruction:\n",
+    "input_suffix": "\n### Response:\n",
+    "antiprompt": [
+      "### Instruction:"
+    ],
+    "pre_prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.",
+    "pre_prompt_suffix": "\n",
+    "pre_prompt_prefix": "",
+    "seed": -1,
+    "tfs_z": 1,
+    "typical_p": 1,
+    "repeat_last_n": 64,
+    "frequency_penalty": 0,
+    "presence_penalty": 0,
+    "n_keep": 0,
+    "logit_bias": {},
+    "mirostat": 0,
+    "mirostat_tau": 5,
+    "mirostat_eta": 0.1,
+    "memory_f16": true,
+    "multiline_input": false,
+    "penalize_nl": true
+  }
+}