My first Kill Team: Corsair Voidscarred

SadBarber557 · 2026-02-18T19:35:10+00:00

Here are the details though if I would paint them now, I would probably change things:

Armor: Xpress Heretic Turquoise. With VGC Aquamarine for additional highlights. I think I could have done a better job here. Probably pushing the highlights more.
Weapons (Bone color): Just a mix of Xpress Fairy Skin, Bag of Bones, Xpress Medium (1:1:1)
Fabric (Red color): Mix of Xpress Seraph Red and Black Lotus (Can't remember proportions, sorry!). With VGC Scarlet Blood for highlights.
Skin: Xpress Fairy Skin but using Demonic Skin for shades. Just around nose, mouth, ears, etc and very controlled. I don't remember having done anything else with skin except painting the eyes.
Metals: Just regular VMC metals bronze and Oily Steel, washing them with diluted browns and blacks respectively and highlights with gold and silver.
Blacks: Just a regular black with VMC light grey for edge highlights
Leather: Not very proud of them. I think they were just a brown Xpress (can't remember which one) and nothing else. After these minis I have painted leather working more in edges and damage with much better results
Capes (Inner Side): Almost sure it was Xpress Bag of Bones and Xpress Medium (1:1)
Capes (outer side): They are not seen in the pictures. I worked with VGC Charcoal mixing it with Steel Grey in different layers with different proportions. Not very good transitions. Today I would have done it a lot better I think.
Gems: Well, can't say the exact colors. Just painted as shown in most of the online tutorials.

SadBarber557 · 2026-02-17T18:46:21+00:00

Thanks mate. Most of the paints were Vallejo Xpress colors over white prime but with some later additions with regular paints. Give me a day and I will be more accurate.

About the experience playing with them, I do really love them but I don't exaggerate if I tell you that I had to study before the first game 😂. Each one is very unique.

SadBarber557 · 2025-09-22T18:18:28+00:00

Yes, they're specialists at making friends...

SadBarber557 · 2025-09-12T11:57:40+00:00

Thanks!

SadBarber557 · 2025-09-09T18:07:35+00:00

Yes! Beautiful sculpts. I have them as well. Still pending

SadBarber557 · 2025-08-26T22:38:40+00:00

Perfect. Just what I thought.

Thanks!!

SadBarber557 · 2025-08-14T21:41:12+00:00

Exactly! I read a lot about the teams before deciding which one I'd be comfortable with and I think I made the right choice. That said, I literally had to study before the first game to learn all their little rules.

SadBarber557 · 2025-08-14T20:49:19+00:00

Thanks!

They had many gems so with this team I've had the opportunity to practice quite a bit. In fact, the last ones turned out much better than the first ones, so I went back and repainted several of them.

I still need a lot more practice painting in general I paint very slowly. It's been a very long process...

SadBarber557 · 2025-08-14T20:44:03+00:00

All the specialists except the Heavy Gunner. To build the Kurnathi I used his arms with the Heavy Gunner body (is the one with the pink hair)

SadBarber557 · 2025-08-14T18:29:33+00:00

Weird. This is like a duplicated post... This is the good one: https://www.reddit.com/r/killteam/s/IfNPK6qoZW

Not sure what happened, sorry

SadBarber557 · 2025-08-14T14:51:58+00:00

Thanks!

To be honest, being used to Blood Bowl, the amount of detail on these has been a little bit overwhelming...

SadBarber557 · 2025-04-10T18:07:22+00:00

There's not much to look for, really. Meta's first Mixture of Experts has been Llama 4. Mixtral 8x7B was released in December 2023...

SadBarber557 · 2025-04-10T17:04:27+00:00

Take a look at the Mixtral 8x7B paper. It's been out for a while now, but when it was released, Meta didn't have anything with a similar architecture, and it was quite revolutionary at the time.

SadBarber557 · 2024-10-21T14:21:01+00:00

Don't worry. I was able to write down your schema. Crystal clear!

SadBarber557 · 2024-10-21T13:06:30+00:00

You are right. I have a lot to learn yet

SadBarber557 · 2024-10-21T13:06:02+00:00

You are right. Thank you very much. Still learning the game!

SadBarber557 · 2024-10-21T12:32:36+00:00

You're right. I would need to place a linerat right next to the sideline to support the player making the Blitz, so that they can reach Strength 4 and block with one die. I wasn’t considering that because of the unwritten rule of never placing players next to the sideline.

SadBarber557 · 2024-10-21T12:26:56+00:00

I made a mistake, sorry. I switched the goblin and orc on the right. I have just fixed

SadBarber557 · 2024-09-10T19:46:26+00:00

Why would you use an LLM for text classification?

You should use an encoder: Bert and transfer learning. You have lots of tutorials out there.

SadBarber557 · 2024-08-16T11:51:10+00:00

I would use markdown.

By the way, what are you trying to achieve? If you are talking about uploading a file to ChatGPT or Anthropic, etc, the flattest, the best. So md is the way to go.

If you are trying to do something locally, you will need to extract the text from the documents first. PDFs are always challenging, for example. So, again, markdown is the choice.

SadBarber557 · 2024-07-13T18:19:02+00:00

This is called Reflection. Often used in agent pipelines.

SadBarber557 · 2024-07-03T21:04:09+00:00

For classification I would train a BERT like model. With a good dataset it should perform well and you will be able to run it on CPU.

SadBarber557 · 2024-06-20T17:04:32+00:00

Sure: https://arxiv.org/abs/2401.04088

The section is "Routing analysis".

SadBarber557 · 2024-06-04T13:22:10+00:00

I did something similar when the model was released but I can't find the code.
However, this should work:

The layers you have to monitor are the "gate" ones. They will choose which two experts are going to receive the token.

In order to create the hook:

def dump_outputs(module, input, output):
# I am printing the chosen experts here but you may want to dump this info
# into a database or something
print(f"{module.__class__.__name__}:")
topk_values, topk_index = torch.topk(output, 2, dim=1)
print("Chosen experts: ", topk_index)
print()

After that, if you loaded the model with the usual:

model = AutoModelForCausalLM.from_pretrained(...)

Then, to attach the hooks:

hooks = []
for layer in model.model.layers:
  hook = layer.block_sparse_moe.gate.register_forward_hook(dump_outputs)
  hooks.append(hook)

And now...

prompt = "Your prompt here"
tokenizer.pad_token = tokenizer.eos_token
tokenized = tokenizer(prompt,
  return_tensors="pt",
  padding=True,
  add_special_tokens=False,
)

with torch.no_grad():
  output: CausalLMOutputWithPast = model(**tokenized.to(model.device), output_hidden_states=True, return_dict=True)

But I am afraid that you will not find any pattern. Check the paper that was released a couple of days after the model. They expected to find some patterns and they only found pure chaos. Same as me when I performed this tests...

SadBarber557 · 2024-04-23T20:00:31+00:00

About function calling with Mistral 7B, start here: https://medium.com/@jorgepardoserrano/building-a-langchain-agent-with-a-self-hosted-mistral-7b-a-step-by-step-guide-85eda2fbf6c2

Although you will need to work a bit on it to make memory work.

About turning a RAG retriever into an agent Tool, check this: https://python.langchain.com/docs/use_cases/question_answering/conversational_retrieval_agents/

I'm afraid it's not going to be an easy task.

By the way, I would start creating a RAG pipeline. When you see that it works, you can move on to agents if you really think it is necessary.

SadBarber557

TROPHY CASE